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Only Train Once: A One-Shot Neural Network Training And Pruning Framework
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https://papers.nips.cc/paper_files/paper/2021/hash/a376033f78e144f494bfc743c0be3330-Abstract.html
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Tianyi Chen, Bo Ji, Tianyu Ding, Biyi Fang, Guanyi Wang, Zhihui Zhu, Luming Liang, Yixin Shi, Sheng Yi, Xiao Tu
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https://papers.nips.cc/paper_files/paper/2021/hash/a376033f78e144f494bfc743c0be3330-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13125-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a376033f78e144f494bfc743c0be3330-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=p5rMPjrcCZq
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https://papers.nips.cc/paper_files/paper/2021/file/a376033f78e144f494bfc743c0be3330-Supplemental.pdf
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Structured pruning is a commonly used technique in deploying deep neural networks (DNNs) onto resource-constrained devices. However, the existing pruning methods are usually heuristic, task-specified, and require an extra fine-tuning procedure. To overcome these limitations, we propose a framework that compresses DNNs into slimmer architectures with competitive performances and significant FLOPs reductions by Only-Train-Once (OTO). OTO contains two key steps: (i) we partition the parameters of DNNs into zero-invariant groups, enabling us to prune zero groups without affecting the output; and (ii) to promote zero groups, we then formulate a structured-sparsity optimization problem, and propose a novel optimization algorithm, Half-Space Stochastic Projected Gradient (HSPG), to solve it, which outperforms the standard proximal methods on group sparsity exploration, and maintains comparable convergence. To demonstrate the effectiveness of OTO, we train and compress full models simultaneously from scratch without fine-tuning for inference speedup and parameter reduction, and achieve state-of-the-art results on VGG16 for CIFAR10, ResNet50 for CIFAR10 and Bert for SQuAD and competitive result on ResNet50 for ImageNet. The source code is available at https://github.com/tianyic/onlytrainonce.
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Referring Transformer: A One-step Approach to Multi-task Visual Grounding
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https://papers.nips.cc/paper_files/paper/2021/hash/a376802c0811f1b9088828288eb0d3f0-Abstract.html
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Muchen Li, Leonid Sigal
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https://papers.nips.cc/paper_files/paper/2021/hash/a376802c0811f1b9088828288eb0d3f0-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13126-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a376802c0811f1b9088828288eb0d3f0-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=J64lDCrYGi
| null |
As an important step towards visual reasoning, visual grounding (e.g., phrase localization, referring expression comprehension / segmentation) has been widely explored. Previous approaches to referring expression comprehension (REC) or segmentation (RES) either suffer from limited performance, due to a two-stage setup, or require the designing of complex task-specific one-stage architectures. In this paper, we propose a simple one-stage multi-task framework for visual grounding tasks. Specifically, we leverage a transformer architecture, where two modalities are fused in a visual-lingual encoder. In the decoder, the model learns to generate contextualized lingual queries which are then decoded and used to directly regress the bounding box and produce a segmentation mask for the corresponding referred regions. With this simple but highly contextualized model, we outperform state-of-the-art methods by a large margin on both REC and RES tasks. We also show that a simple pre-training schedule (on an external dataset) further improves the performance. Extensive experiments and ablations illustrate that our model benefits greatly from contextualized information and multi-task training.
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Decoupling the Depth and Scope of Graph Neural Networks
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https://papers.nips.cc/paper_files/paper/2021/hash/a378383b89e6719e15cd1aa45478627c-Abstract.html
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Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, Ren Chen
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https://papers.nips.cc/paper_files/paper/2021/hash/a378383b89e6719e15cd1aa45478627c-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13127-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a378383b89e6719e15cd1aa45478627c-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=_IY3_4psXuf
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https://papers.nips.cc/paper_files/paper/2021/file/a378383b89e6719e15cd1aa45478627c-Supplemental.pdf
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State-of-the-art Graph Neural Networks (GNNs) have limited scalability with respect to the graph and model sizes. On large graphs, increasing the model depth often means exponential expansion of the scope (i.e., receptive field). Beyond just a few layers, two fundamental challenges emerge: 1. degraded expressivity due to oversmoothing, and 2. expensive computation due to neighborhood explosion. We propose a design principle to decouple the depth and scope of GNNs – to generate representation of a target entity (i.e., a node or an edge), we first extract a localized subgraph as the bounded-size scope, and then apply a GNN of arbitrary depth on top of the subgraph. A properly extracted subgraph consists of a small number of critical neighbors, while excluding irrelevant ones. The GNN, no matter how deep it is, smooths the local neighborhood into informative representation rather than oversmoothing the global graph into “white noise”. Theoretically, decoupling improves the GNN expressive power from the perspectives of graph signal processing (GCN), function approximation (GraphSAGE) and topological learning (GIN). Empirically, on seven graphs (with up to 110M nodes) and six backbone GNN architectures, our design achieves significant accuracy improvement with orders of magnitude reduction in computation and hardware cost.
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Fast and Memory Efficient Differentially Private-SGD via JL Projections
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https://papers.nips.cc/paper_files/paper/2021/hash/a3842ed7b3d0fe3ac263bcabd2999790-Abstract.html
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Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Hanwen Shen, Uthaipon Tantipongpipat
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https://papers.nips.cc/paper_files/paper/2021/hash/a3842ed7b3d0fe3ac263bcabd2999790-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13128-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a3842ed7b3d0fe3ac263bcabd2999790-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=WwZbupAKWo
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https://papers.nips.cc/paper_files/paper/2021/file/a3842ed7b3d0fe3ac263bcabd2999790-Supplemental.pdf
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Differentially Private-SGD (DP-SGD) of Abadi et al. and its variations are the only known algorithms for private training of large scale neural networks. This algorithm requires computation of per-sample gradients norms which is extremely slow and memory intensive in practice. In this paper, we present a new framework to design differentially private optimizers called DP-SGD-JL and DP-Adam-JL. Our approach uses Johnson–Lindenstrauss (JL) projections to quickly approximate the per-sample gradient norms without exactly computing them, thus making the training time and memory requirements of our optimizers closer to that of their non-DP versions. Unlike previous attempts to make DP-SGD faster which work only on a subset of network architectures or use compiler techniques, we propose an algorithmic solution which works for any network in a black-box manner which is the main contribution of this paper. To illustrate this, on IMDb dataset, we train a Recurrent Neural Network (RNN) to achieve good privacy-vs-accuracy tradeoff, while being significantly faster than DP-SGD and with a similar memory footprint as non-private SGD.
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Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations
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https://papers.nips.cc/paper_files/paper/2021/hash/a3ab4ff8fa4deed2e3bae3a5077675f0-Abstract.html
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Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu, Pin-Yu Chen
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https://papers.nips.cc/paper_files/paper/2021/hash/a3ab4ff8fa4deed2e3bae3a5077675f0-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13129-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a3ab4ff8fa4deed2e3bae3a5077675f0-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=hOG8swMRmY
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https://papers.nips.cc/paper_files/paper/2021/file/a3ab4ff8fa4deed2e3bae3a5077675f0-Supplemental.pdf
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Studying the sensitivity of weight perturbation in neural networks and its impacts on model performance, including generalization and robustness, is an active research topic due to its implications on a wide range of machine learning tasks such as model compression, generalization gap assessment, and adversarial attacks. In this paper, we provide the first integral study and analysis for feed-forward neural networks in terms of the robustness in pairwise class margin and its generalization behavior under weight perturbation. We further design a new theory-driven loss function for training generalizable and robust neural networks against weight perturbations. Empirical experiments are conducted to validate our theoretical analysis. Our results offer fundamental insights for characterizing the generalization and robustness of neural networks against weight perturbations.
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Pipeline Combinators for Gradual AutoML
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https://papers.nips.cc/paper_files/paper/2021/hash/a3b36cb25e2e0b93b5f334ffb4e4064e-Abstract.html
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Guillaume Baudart, Martin Hirzel, Kiran Kate, Parikshit Ram, Avi Shinnar, Jason Tsay
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https://papers.nips.cc/paper_files/paper/2021/hash/a3b36cb25e2e0b93b5f334ffb4e4064e-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13130-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a3b36cb25e2e0b93b5f334ffb4e4064e-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=uFORMPcA_b
| null |
Automated machine learning (AutoML) can make data scientists more productive. But if machine learning is totally automated, that leaves no room for data scientists to apply their intuition. Hence, data scientists often prefer not total but gradual automation, where they control certain choices and AutoML explores the rest. Unfortunately, gradual AutoML is cumbersome with state-of-the-art tools, requiring large non-compositional code changes. More concise compositional code can be achieved with combinators, a powerful concept from functional programming. This paper introduces a small set of orthogonal combinators for composing machine-learning operators into pipelines. It describes a translation scheme from pipelines and associated hyperparameter schemas to search spaces for AutoML optimizers. On that foundation, this paper presents Lale, an open-source sklearn-compatible AutoML library, and evaluates it with a user study.
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Boost Neural Networks by Checkpoints
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https://papers.nips.cc/paper_files/paper/2021/hash/a40511cad8383e5ae8ddd8b855d135da-Abstract.html
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Feng Wang, Guoyizhe Wei, Qiao Liu, Jinxiang Ou, xian wei, Hairong Lv
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https://papers.nips.cc/paper_files/paper/2021/hash/a40511cad8383e5ae8ddd8b855d135da-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13131-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a40511cad8383e5ae8ddd8b855d135da-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=zaqGp90Od4y
| null |
Training multiple deep neural networks (DNNs) and averaging their outputs is a simple way to improve the predictive performance. Nevertheless, the multiplied training cost prevents this ensemble method to be practical and efficient. Several recent works attempt to save and ensemble the checkpoints of DNNs, which only requires the same computational cost as training a single network. However, these methods suffer from either marginal accuracy improvements due to the low diversity of checkpoints or high risk of divergence due to the cyclical learning rates they adopted. In this paper, we propose a novel method to ensemble the checkpoints, where a boosting scheme is utilized to accelerate model convergence and maximize the checkpoint diversity. We theoretically prove that it converges by reducing exponential loss. The empirical evaluation also indicates our proposed ensemble outperforms single model and existing ensembles in terms of accuracy and efficiency. With the same training budget, our method achieves 4.16% lower error on Cifar-100 and 6.96% on Tiny-ImageNet with ResNet-110 architecture. Moreover, the adaptive sample weights in our method make it an effective solution to address the imbalanced class distribution. In the experiments, it yields up to 5.02% higher accuracy over single EfficientNet-B0 on the imbalanced datasets.
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Model Selection for Bayesian Autoencoders
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https://papers.nips.cc/paper_files/paper/2021/hash/a41db61e2728ef963614a8c8755b9b9a-Abstract.html
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Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone
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https://papers.nips.cc/paper_files/paper/2021/hash/a41db61e2728ef963614a8c8755b9b9a-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13132-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a41db61e2728ef963614a8c8755b9b9a-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=KPLf9FhwEqZ
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https://papers.nips.cc/paper_files/paper/2021/file/a41db61e2728ef963614a8c8755b9b9a-Supplemental.pdf
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We develop a novel method for carrying out model selection for Bayesian autoencoders (BAEs) by means of prior hyper-parameter optimization. Inspired by the common practice of type-II maximum likelihood optimization and its equivalence to Kullback-Leibler divergence minimization, we propose to optimize the distributional sliced-Wasserstein distance (DSWD) between the output of the autoencoder and the empirical data distribution. The advantages of this formulation are that we can estimate the DSWD based on samples and handle high-dimensional problems. We carry out posterior estimation of the BAE parameters via stochastic gradient Hamiltonian Monte Carlo and turn our BAE into a generative model by fitting a flexible Dirichlet mixture model in the latent space. Thanks to this approach, we obtain a powerful alternative to variational autoencoders, which are the preferred choice in modern application of autoencoders for representation learning with uncertainty. We evaluate our approach qualitatively and quantitatively using a vast experimental campaign on a number of unsupervised learning tasks and show that, in small-data regimes where priors matter, our approach provides state-of-the-art results, outperforming multiple competitive baselines.
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Three Operator Splitting with Subgradients, Stochastic Gradients, and Adaptive Learning Rates
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https://papers.nips.cc/paper_files/paper/2021/hash/a4267159aa970aa5a6542bcbb7ef575e-Abstract.html
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Alp Yurtsever, Alex Gu, Suvrit Sra
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https://papers.nips.cc/paper_files/paper/2021/hash/a4267159aa970aa5a6542bcbb7ef575e-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13133-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a4267159aa970aa5a6542bcbb7ef575e-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=crnXK0jC2F
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https://papers.nips.cc/paper_files/paper/2021/file/a4267159aa970aa5a6542bcbb7ef575e-Supplemental.pdf
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Three Operator Splitting (TOS) (Davis & Yin, 2017) can minimize the sum of multiple convex functions effectively when an efficient gradient oracle or proximal operator is available for each term. This requirement often fails in machine learning applications: (i) instead of full gradients only stochastic gradients may be available; and (ii) instead of proximal operators, using subgradients to handle complex penalty functions may be more efficient and realistic. Motivated by these concerns, we analyze three potentially valuable extensions of TOS. The first two permit using subgradients and stochastic gradients, and are shown to ensure a $\mathcal{O}(1/\sqrt{t})$ convergence rate. The third extension AdapTOS endows TOS with adaptive step-sizes. For the important setting of optimizing a convex loss over the intersection of convex sets AdapTOS attains universal convergence rates, i.e., the rate adapts to the unknown smoothness degree of the objective. We compare our proposed methods with competing methods on various applications.
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Knowledge-Adaptation Priors
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https://papers.nips.cc/paper_files/paper/2021/hash/a4380923dd651c195b1631af7c829187-Abstract.html
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Mohammad Emtiyaz Khan, Siddharth Swaroop
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https://papers.nips.cc/paper_files/paper/2021/hash/a4380923dd651c195b1631af7c829187-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13134-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a4380923dd651c195b1631af7c829187-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=_cXX-Dr7sf0
|
https://papers.nips.cc/paper_files/paper/2021/file/a4380923dd651c195b1631af7c829187-Supplemental.pdf
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Humans and animals have a natural ability to quickly adapt to their surroundings, but machine-learning models, when subjected to changes, often require a complete retraining from scratch. We present Knowledge-adaptation priors (K-priors) to reduce the cost of retraining by enabling quick and accurate adaptation for a wide-variety of tasks and models. This is made possible by a combination of weight and function-space priors to reconstruct the gradients of the past, which recovers and generalizes many existing, but seemingly-unrelated, adaptation strategies. Training with simple first-order gradient methods can often recover the exact retrained model to an arbitrary accuracy by choosing a sufficiently large memory of the past data. Empirical results show that adaptation with K-priors achieves performance similar to full retraining, but only requires training on a handful of past examples.
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Provably efficient multi-task reinforcement learning with model transfer
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https://papers.nips.cc/paper_files/paper/2021/hash/a440a3d316c5614c7a9310e902f4a43e-Abstract.html
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Chicheng Zhang, Zhi Wang
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https://papers.nips.cc/paper_files/paper/2021/hash/a440a3d316c5614c7a9310e902f4a43e-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13135-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a440a3d316c5614c7a9310e902f4a43e-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=qPOeyokHXT8
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https://papers.nips.cc/paper_files/paper/2021/file/a440a3d316c5614c7a9310e902f4a43e-Supplemental.pdf
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We study multi-task reinforcement learning (RL) in tabular episodic Markov decision processes (MDPs). We formulate a heterogeneous multi-player RL problem, in which a group of players concurrently face similar but not necessarily identical MDPs, with a goal of improving their collective performance through inter-player information sharing. We design and analyze a model-based algorithm, and provide gap-dependent and gap-independent regret upper and lower bounds that characterize the intrinsic complexity of the problem.
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Predicting Molecular Conformation via Dynamic Graph Score Matching
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https://papers.nips.cc/paper_files/paper/2021/hash/a45a1d12ee0fb7f1f872ab91da18f899-Abstract.html
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Shitong Luo, Chence Shi, Minkai Xu, Jian Tang
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https://papers.nips.cc/paper_files/paper/2021/hash/a45a1d12ee0fb7f1f872ab91da18f899-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13136-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a45a1d12ee0fb7f1f872ab91da18f899-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=hMY6nm9lld
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https://papers.nips.cc/paper_files/paper/2021/file/a45a1d12ee0fb7f1f872ab91da18f899-Supplemental.pdf
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Predicting stable 3D conformations from 2D molecular graphs has been a long-standing challenge in computational chemistry. Recently, machine learning approaches have demonstrated very promising results compared to traditional experimental and physics-based simulation methods. These approaches mainly focus on modeling the local interactions between neighboring atoms on the molecular graphs and overlook the long-range interactions between non-bonded atoms. However, these non-bonded atoms may be proximal to each other in 3D space, and modeling their interactions is of crucial importance to accurately determine molecular conformations, especially for large molecules and multi-molecular complexes. In this paper, we propose a new approach called Dynamic Graph Score Matching (DGSM) for molecular conformation prediction, which models both the local and long-range interactions by dynamically constructing graph structures between atoms according to their spatial proximity during both training and inference. Specifically, the DGSM directly estimates the gradient fields of the logarithm density of atomic coordinates according to the dynamically constructed graphs using score matching methods. The whole framework can be efficiently trained in an end-to-end fashion. Experiments across multiple tasks show that the DGSM outperforms state-of-the-art baselines by a large margin, and it is capable of generating conformations for a broader range of systems such as proteins and multi-molecular complexes.
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When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting
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https://papers.nips.cc/paper_files/paper/2021/hash/a4a1108bbcc329a70efa93d7bf060914-Abstract.html
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Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodriguez, Chao Zhang, B. Aditya Prakash
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https://papers.nips.cc/paper_files/paper/2021/hash/a4a1108bbcc329a70efa93d7bf060914-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13137-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a4a1108bbcc329a70efa93d7bf060914-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=CONAi0Bh26d
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https://papers.nips.cc/paper_files/paper/2021/file/a4a1108bbcc329a70efa93d7bf060914-Supplemental.pdf
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Accurate and trustworthy epidemic forecasting is an important problem for public health planning and disease mitigation. Most existing epidemic forecasting models disregard uncertainty quantification, resulting in mis-calibrated predictions. Recent works in deep neural models for uncertainty-aware time-series forecasting also have several limitations; e.g., it is difficult to specify proper priors in Bayesian NNs, while methods like deep ensembling can be computationally expensive. In this paper, we propose to use neural functional processes to fill this gap. We model epidemic time-series with a probabilistic generative process and propose a functional neural process model called EpiFNP, which directly models the probability distribution of the forecast value in a non-parametric way. In EpiFNP, we use a dynamic stochastic correlation graph to model the correlations between sequences, and design different stochastic latent variables to capture functional uncertainty from different perspectives. Our experiments in a real-time flu forecasting setting show that EpiFNP significantly outperforms state-of-the-art models in both accuracy and calibration metrics, up to 2.5x in accuracy and 2.4x in calibration. Additionally, as EpiFNP learns the relations between the current season and similar patterns of historical seasons, it enables interpretable forecasts. Beyond epidemic forecasting, EpiFNP can be of independent interest for advancing uncertainty quantification in deep sequential models for predictive analytics.
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Bounds all around: training energy-based models with bidirectional bounds
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https://papers.nips.cc/paper_files/paper/2021/hash/a4d8e2a7e0d0c102339f97716d2fdfb6-Abstract.html
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Cong Geng, Jia Wang, Zhiyong Gao, Jes Frellsen, Søren Hauberg
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https://papers.nips.cc/paper_files/paper/2021/hash/a4d8e2a7e0d0c102339f97716d2fdfb6-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13138-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a4d8e2a7e0d0c102339f97716d2fdfb6-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=HX0eMs5YYMA
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https://papers.nips.cc/paper_files/paper/2021/file/a4d8e2a7e0d0c102339f97716d2fdfb6-Supplemental.pdf
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Energy-based models (EBMs) provide an elegant framework for density estimation, but they are notoriously difficult to train. Recent work has established links to generative adversarial networks, where the EBM is trained through a minimax game with a variational value function. We propose a bidirectional bound on the EBM log-likelihood, such that we maximize a lower bound and minimize an upper bound when solving the minimax game. We link one bound to a gradient penalty that stabilizes training, thereby provide grounding for best engineering practice. To evaluate the bounds we develop a new and efficient estimator of the Jacobi-determinant of the EBM generator. We demonstrate that these developments stabilize training and yield high-quality density estimation and sample generation.
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CogView: Mastering Text-to-Image Generation via Transformers
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https://papers.nips.cc/paper_files/paper/2021/hash/a4d92e2cd541fca87e4620aba658316d-Abstract.html
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Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, Jie Tang
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https://papers.nips.cc/paper_files/paper/2021/hash/a4d92e2cd541fca87e4620aba658316d-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13139-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a4d92e2cd541fca87e4620aba658316d-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=cnWSyJNmeCE
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https://papers.nips.cc/paper_files/paper/2021/file/a4d92e2cd541fca87e4620aba658316d-Supplemental.pdf
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Text-to-Image generation in the general domain has long been an open problem, which requires both a powerful generative model and cross-modal understanding. We propose CogView, a 4-billion-parameter Transformer with VQ-VAE tokenizer to advance this problem. We also demonstrate the finetuning strategies for various downstream tasks, e.g. style learning, super-resolution, text-image ranking and fashion design, and methods to stabilize pretraining, e.g. eliminating NaN losses. CogView achieves the state-of-the-art FID on the blurred MS COCO dataset, outperforming previous GAN-based models and a recent similar work DALL-E.
| null |
Time-independent Generalization Bounds for SGLD in Non-convex Settings
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https://papers.nips.cc/paper_files/paper/2021/hash/a4ee59dd868ba016ed2de90d330acb6a-Abstract.html
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Tyler Farghly, Patrick Rebeschini
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https://papers.nips.cc/paper_files/paper/2021/hash/a4ee59dd868ba016ed2de90d330acb6a-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13140-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a4ee59dd868ba016ed2de90d330acb6a-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=tNT4APQ0Wgj
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https://papers.nips.cc/paper_files/paper/2021/file/a4ee59dd868ba016ed2de90d330acb6a-Supplemental.pdf
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We establish generalization error bounds for stochastic gradient Langevin dynamics (SGLD) with constant learning rate under the assumptions of dissipativity and smoothness, a setting that has received increased attention in the sampling/optimization literature. Unlike existing bounds for SGLD in non-convex settings, ours are time-independent and decay to zero as the sample size increases. Using the framework of uniform stability, we establish time-independent bounds by exploiting the Wasserstein contraction property of the Langevin diffusion, which also allows us to circumvent the need to bound gradients using Lipschitz-like assumptions. Our analysis also supports variants of SGLD that use different discretization methods, incorporate Euclidean projections, or use non-isotropic noise.
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Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data
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https://papers.nips.cc/paper_files/paper/2021/hash/a51c896c9cb81ecb5a199d51ac9fc3c5-Abstract.html
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HaiYing Wang, Aonan Zhang, Chong Wang
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https://papers.nips.cc/paper_files/paper/2021/hash/a51c896c9cb81ecb5a199d51ac9fc3c5-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13141-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a51c896c9cb81ecb5a199d51ac9fc3c5-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=v4vuGbNIv71
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https://papers.nips.cc/paper_files/paper/2021/file/a51c896c9cb81ecb5a199d51ac9fc3c5-Supplemental.pdf
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We investigate the issue of parameter estimation with nonuniform negative sampling for imbalanced data. We first prove that, with imbalanced data, the available information about unknown parameters is only tied to the relatively small number of positive instances, which justifies the usage of negative sampling. However, if the negative instances are subsampled to the same level of the positive cases, there is information loss. To maintain more information, we derive the asymptotic distribution of a general inverse probability weighted (IPW) estimator and obtain the optimal sampling probability that minimizes its variance. To further improve the estimation efficiency over the IPW method, we propose a likelihood-based estimator by correcting log odds for the sampled data and prove that the improved estimator has the smallest asymptotic variance among a large class of estimators. It is also more robust to pilot misspecification. We validate our approach on simulated data as well as a real click-through rate dataset with more than 0.3 trillion instances, collected over a period of a month. Both theoretical and empirical results demonstrate the effectiveness of our method.
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Algorithmic stability and generalization of an unsupervised feature selection algorithm
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https://papers.nips.cc/paper_files/paper/2021/hash/a546203962b88771bb06faf8d6ec065e-Abstract.html
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xinxing wu, Qiang Cheng
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https://papers.nips.cc/paper_files/paper/2021/hash/a546203962b88771bb06faf8d6ec065e-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13142-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a546203962b88771bb06faf8d6ec065e-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=huxo_Mqh76
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https://papers.nips.cc/paper_files/paper/2021/file/a546203962b88771bb06faf8d6ec065e-Supplemental.pdf
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Feature selection, as a vital dimension reduction technique, reduces data dimension by identifying an essential subset of input features, which can facilitate interpretable insights into learning and inference processes. Algorithmic stability is a key characteristic of an algorithm regarding its sensitivity to perturbations of input samples. In this paper, we propose an innovative unsupervised feature selection algorithm attaining this stability with provable guarantees. The architecture of our algorithm consists of a feature scorer and a feature selector. The scorer trains a neural network (NN) to globally score all the features, and the selector adopts a dependent sub-NN to locally evaluate the representation abilities for selecting features. Further, we present algorithmic stability analysis and show that our algorithm has a performance guarantee via a generalization error bound. Extensive experimental results on real-world datasets demonstrate superior generalization performance of our proposed algorithm to strong baseline methods. Also, the properties revealed by our theoretical analysis and the stability of our algorithm-selected features are empirically confirmed.
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On learning sparse vectors from mixture of responses
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https://papers.nips.cc/paper_files/paper/2021/hash/a5481cd6d7517aa3fc6476dc7d9019ab-Abstract.html
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Nikita Polyanskii
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https://papers.nips.cc/paper_files/paper/2021/hash/a5481cd6d7517aa3fc6476dc7d9019ab-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13143-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a5481cd6d7517aa3fc6476dc7d9019ab-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=6k0bAbb6m6
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https://papers.nips.cc/paper_files/paper/2021/file/a5481cd6d7517aa3fc6476dc7d9019ab-Supplemental.pdf
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In this paper, we address two learning problems. Suppose a family of $\ell$ unknown sparse vectors is fixed, where each vector has at most $k$ non-zero elements. In the first problem, we concentrate on robust learning the supports of all vectors from the family using a sequence of noisy responses. Each response to a query vector shows the sign of the inner product between a randomly chosen vector from the family and the query vector. In the second problem, we aim at designing queries such that all sparse vectors from the family can be approximately reconstructed based on the error-free responses. This learning model was introduced in the work of Gandikota et al., 2020, and these problems can be seen as generalizations of support recovery and approximate recovery problems, well-studied under the framework of 1-bit compressed sensing. As the main contribution of the paper, we prove the existence of learning algorithms for the first problem which work without any assumptions. Under a mild structural assumption on the unknown vectors, we also show the existence of learning algorithms for the second problem and rigorously analyze their query complexity.
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Convergence and Alignment of Gradient Descent with Random Backpropagation Weights
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https://papers.nips.cc/paper_files/paper/2021/hash/a576eafbce762079f7d1f77fca1c5cc2-Abstract.html
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Ganlin Song, Ruitu Xu, John Lafferty
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https://papers.nips.cc/paper_files/paper/2021/hash/a576eafbce762079f7d1f77fca1c5cc2-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13144-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a576eafbce762079f7d1f77fca1c5cc2-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=1QhRTsqYPB
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https://papers.nips.cc/paper_files/paper/2021/file/a576eafbce762079f7d1f77fca1c5cc2-Supplemental.pdf
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Stochastic gradient descent with backpropagation is the workhorse of artificial neural networks. It has long been recognized that backpropagation fails to be a biologically plausible algorithm. Fundamentally, it is a non-local procedure---updating one neuron's synaptic weights requires knowledge of synaptic weights or receptive fields of downstream neurons. This limits the use of artificial neural networks as a tool for understanding the biological principles of information processing in the brain. Lillicrap et al. (2016) propose a more biologically plausible "feedback alignment" algorithm that uses random and fixed backpropagation weights, and show promising simulations. In this paper we study the mathematical properties of the feedback alignment procedure by analyzing convergence and alignment for two-layer networks under squared error loss. In the overparameterized setting, we prove that the error converges to zero exponentially fast, and also that regularization is necessary in order for the parameters to become aligned with the random backpropagation weights. Simulations are given that are consistent with this analysis and suggest further generalizations. These results contribute to our understanding of how biologically plausible algorithms might carry out weight learning in a manner different from Hebbian learning, with performance that is comparable with the full non-local backpropagation algorithm.
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Adder Attention for Vision Transformer
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https://papers.nips.cc/paper_files/paper/2021/hash/a57e8915461b83adefb011530b711704-Abstract.html
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Han Shu, Jiahao Wang, Hanting Chen, Lin Li, Yujiu Yang, Yunhe Wang
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https://papers.nips.cc/paper_files/paper/2021/hash/a57e8915461b83adefb011530b711704-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13145-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a57e8915461b83adefb011530b711704-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=5Ld5bRB9jzY
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https://papers.nips.cc/paper_files/paper/2021/file/a57e8915461b83adefb011530b711704-Supplemental.pdf
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Transformer is a new kind of calculation paradigm for deep learning which has shown strong performance on a large variety of computer vision tasks. However, compared with conventional deep models (e.g., convolutional neural networks), vision transformers require more computational resources which cannot be easily deployed on mobile devices. To this end, we present to reduce the energy consumptions using adder neural network (AdderNet). We first theoretically analyze the mechanism of self-attention and the difficulty for applying adder operation into this module. Specifically, the feature diversity, i.e., the rank of attention map using only additions cannot be well preserved. Thus, we develop an adder attention layer that includes an additional identity mapping. With the new operation, vision transformers constructed using additions can also provide powerful feature representations. Experimental results on several benchmarks demonstrate that the proposed approach can achieve highly competitive performance to that of the baselines while achieving an about 2~3× reduction on the energy consumption.
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Reverse engineering learned optimizers reveals known and novel mechanisms
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https://papers.nips.cc/paper_files/paper/2021/hash/a57ecd54d4df7d999bd9c5e3b973ec75-Abstract.html
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Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein
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https://papers.nips.cc/paper_files/paper/2021/hash/a57ecd54d4df7d999bd9c5e3b973ec75-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13146-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a57ecd54d4df7d999bd9c5e3b973ec75-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=gRlsFQMo_ze
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https://papers.nips.cc/paper_files/paper/2021/file/a57ecd54d4df7d999bd9c5e3b973ec75-Supplemental.pdf
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Learned optimizers are parametric algorithms that can themselves be trained to solve optimization problems. In contrast to baseline optimizers (such as momentum or Adam) that use simple update rules derived from theoretical principles, learned optimizers use flexible, high-dimensional, nonlinear parameterizations. Although this can lead to better performance, their inner workings remain a mystery. How is a given learned optimizer able to outperform a well tuned baseline? Has it learned a sophisticated combination of existing optimization techniques, or is it implementing completely new behavior? In this work, we address these questions by careful analysis and visualization of learned optimizers. We study learned optimizers trained from scratch on four disparate tasks, and discover that they have learned interpretable behavior, including: momentum, gradient clipping, learning rate schedules, and new forms of learning rate adaptation. Moreover, we show how dynamics and mechanisms inside of learned optimizers orchestrate these computations. Our results help elucidate the previously murky understanding of how learned optimizers work, and establish tools for interpreting future learned optimizers.
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Matching a Desired Causal State via Shift Interventions
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https://papers.nips.cc/paper_files/paper/2021/hash/a5a61717dddc3501cfdf7a4e22d7dbaa-Abstract.html
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Jiaqi Zhang, Chandler Squires, Caroline Uhler
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https://papers.nips.cc/paper_files/paper/2021/hash/a5a61717dddc3501cfdf7a4e22d7dbaa-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13147-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a5a61717dddc3501cfdf7a4e22d7dbaa-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=Sgqb8b8swh7
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https://papers.nips.cc/paper_files/paper/2021/file/a5a61717dddc3501cfdf7a4e22d7dbaa-Supplemental.pdf
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Transforming a causal system from a given initial state to a desired target state is an important task permeating multiple fields including control theory, biology, and materials science. In causal models, such transformations can be achieved by performing a set of interventions. In this paper, we consider the problem of identifying a shift intervention that matches the desired mean of a system through active learning. We define the Markov equivalence class that is identifiable from shift interventions and propose two active learning strategies that are guaranteed to exactly match a desired mean. We then derive a worst-case lower bound for the number of interventions required and show that these strategies are optimal for certain classes of graphs. In particular, we show that our strategies may require exponentially fewer interventions than the previously considered approaches, which optimize for structure learning in the underlying causal graph. In line with our theoretical results, we also demonstrate experimentally that our proposed active learning strategies require fewer interventions compared to several baselines.
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Unsupervised Noise Adaptive Speech Enhancement by Discriminator-Constrained Optimal Transport
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https://papers.nips.cc/paper_files/paper/2021/hash/a5c7b30fb632c92feb59154517223dc9-Abstract.html
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Hsin-Yi Lin, Huan-Hsin Tseng, Xugang Lu, Yu Tsao
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https://papers.nips.cc/paper_files/paper/2021/hash/a5c7b30fb632c92feb59154517223dc9-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13148-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a5c7b30fb632c92feb59154517223dc9-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=R6U4-Qkcg21
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https://papers.nips.cc/paper_files/paper/2021/file/a5c7b30fb632c92feb59154517223dc9-Supplemental.pdf
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This paper presents a novel discriminator-constrained optimal transport network (DOTN) that performs unsupervised domain adaptation for speech enhancement (SE), which is an essential regression task in speech processing. The DOTN aims to estimate clean references of noisy speech in a target domain, by exploiting the knowledge available from the source domain. The domain shift between training and testing data has been reported to be an obstacle to learning problems in diverse fields. Although rich literature exists on unsupervised domain adaptation for classification, the methods proposed, especially in regressions, remain scarce and often depend on additional information regarding the input data. The proposed DOTN approach tactically fuses the optimal transport (OT) theory from mathematical analysis with generative adversarial frameworks, to help evaluate continuous labels in the target domain. The experimental results on two SE tasks demonstrate that by extending the classical OT formulation, our proposed DOTN outperforms previous adversarial domain adaptation frameworks in a purely unsupervised manner.
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Optimality of variational inference for stochasticblock model with missing links
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https://papers.nips.cc/paper_files/paper/2021/hash/a5e308070bd6dd3cc56283f2313522de-Abstract.html
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Solenne Gaucher, Olga Klopp
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https://papers.nips.cc/paper_files/paper/2021/hash/a5e308070bd6dd3cc56283f2313522de-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13149-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a5e308070bd6dd3cc56283f2313522de-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=iYzsR0JNaa2
|
https://papers.nips.cc/paper_files/paper/2021/file/a5e308070bd6dd3cc56283f2313522de-Supplemental.zip
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Variational methods are extremely popular in the analysis of network data. Statistical guarantees obtained for these methods typically provide asymptotic normality for the problem of estimation of global model parameters under the stochastic block model. In the present work, we consider the case of networks with missing links that is important in application and show that the variational approximation to the maximum likelihood estimator converges at the minimax rate. This provides the first minimax optimal and tractable estimator for the problem of parameter estimation for the stochastic block model with missing links. We complement our results with numerical studies of simulated and real networks, which confirm the advantages of this estimator over current methods.
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Policy Learning Using Weak Supervision
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https://papers.nips.cc/paper_files/paper/2021/hash/a613863f6a3ada47ae5bca2a558872d1-Abstract.html
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Jingkang Wang, Hongyi Guo, Zhaowei Zhu, Yang Liu
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https://papers.nips.cc/paper_files/paper/2021/hash/a613863f6a3ada47ae5bca2a558872d1-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13150-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a613863f6a3ada47ae5bca2a558872d1-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=UZgQhsTYe3R
|
https://papers.nips.cc/paper_files/paper/2021/file/a613863f6a3ada47ae5bca2a558872d1-Supplemental.pdf
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Most existing policy learning solutions require the learning agents to receive high-quality supervision signals, e.g., rewards in reinforcement learning (RL) or high-quality expert demonstrations in behavioral cloning (BC). These quality supervisions are either infeasible or prohibitively expensive to obtain in practice. We aim for a unified framework that leverages the available cheap weak supervisions to perform policy learning efficiently. To handle this problem, we treat the weak supervision'' as imperfect information coming from a peer agent, and evaluate the learning agent's policy based on a correlated agreement'' with the peer agent's policy (instead of simple agreements). Our approach explicitly punishes a policy for overfitting to the weak supervision. In addition to theoretical guarantees, extensive evaluations on tasks including RL with noisy reward, BC with weak demonstrations, and standard policy co-training (RL + BC) show that our method leads to substantial performance improvements, especially when the complexity or the noise of the learning environments is high.
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Chasing Sparsity in Vision Transformers: An End-to-End Exploration
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https://papers.nips.cc/paper_files/paper/2021/hash/a61f27ab2165df0e18cc9433bd7f27c5-Abstract.html
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Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang
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https://papers.nips.cc/paper_files/paper/2021/hash/a61f27ab2165df0e18cc9433bd7f27c5-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13151-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a61f27ab2165df0e18cc9433bd7f27c5-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=LKoMTwTuQnC
|
https://papers.nips.cc/paper_files/paper/2021/file/a61f27ab2165df0e18cc9433bd7f27c5-Supplemental.pdf
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Vision transformers (ViTs) have recently received explosive popularity, but their enormous model sizes and training costs remain daunting. Conventional post-training pruning often incurs higher training budgets. In contrast, this paper aims to trim down both the training memory overhead and the inference complexity, without sacrificing the achievable accuracy. We carry out the first-of-its-kind comprehensive exploration, on taking a unified approach of integrating sparsity in ViTs "from end to end''. Specifically, instead of training full ViTs, we dynamically extract and train sparse subnetworks, while sticking to a fixed small parameter budget. Our approach jointly optimizes model parameters and explores connectivity throughout training, ending up with one sparse network as the final output. The approach is seamlessly extended from unstructured to structured sparsity, the latter by considering to guide the prune-and-grow of self-attention heads inside ViTs. We further co-explore data and architecture sparsity for additional efficiency gains by plugging in a novel learnable token selector to adaptively determine the currently most vital patches. Extensive results on ImageNet with diverse ViT backbones validate the effectiveness of our proposals which obtain significantly reduced computational cost and almost unimpaired generalization. Perhaps most surprisingly, we find that the proposed sparse (co-)training can sometimes \textit{improve the ViT accuracy} rather than compromising it, making sparsity a tantalizing "free lunch''. For example, our sparsified DeiT-Small at ($5\%$, $50\%$) sparsity for (data, architecture), improves $\mathbf{0.28\%}$ top-1 accuracy, and meanwhile enjoys $\mathbf{49.32\%}$ FLOPs and $\mathbf{4.40\%}$ running time savings. Our codes are available at https://github.com/VITA-Group/SViTE.
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Graphical Models in Heavy-Tailed Markets
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https://papers.nips.cc/paper_files/paper/2021/hash/a64a034c3cb8eac64eb46ea474902797-Abstract.html
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Jose Vinicius de Miranda Cardoso, Jiaxi Ying, Daniel Palomar
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https://papers.nips.cc/paper_files/paper/2021/hash/a64a034c3cb8eac64eb46ea474902797-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13152-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a64a034c3cb8eac64eb46ea474902797-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=w1FvEPcwTnI
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https://papers.nips.cc/paper_files/paper/2021/file/a64a034c3cb8eac64eb46ea474902797-Supplemental.pdf
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Heavy-tailed statistical distributions have long been considered a more realistic statistical model for the data generating process in financial markets in comparison to their Gaussian counterpart. Nonetheless, mathematical nuisances, including nonconvexities, involved in estimating graphs in heavy-tailed settings pose a significant challenge to the practical design of algorithms for graph learning. In this work, we present graph learning estimators based on the Markov random field framework that assume a Student-$t$ data generating process. We design scalable numerical algorithms, via the alternating direction method of multipliers, to learn both connected and $k$-component graphs along with their theoretical convergence guarantees. The proposed methods outperform state-of-the-art benchmarks in an extensive series of practical experiments with publicly available data from the S\&P500 index, foreign exchanges, and cryptocurrencies.
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A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis
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https://papers.nips.cc/paper_files/paper/2021/hash/a64c94baaf368e1840a1324e839230de-Abstract.html
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Xingang Pan, Xudong XU, Chen Change Loy, Christian Theobalt, Bo Dai
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https://papers.nips.cc/paper_files/paper/2021/hash/a64c94baaf368e1840a1324e839230de-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13153-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a64c94baaf368e1840a1324e839230de-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=k-0oq5eNjh
|
https://papers.nips.cc/paper_files/paper/2021/file/a64c94baaf368e1840a1324e839230de-Supplemental.zip
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The advancement of generative radiance fields has pushed the boundary of 3D-aware image synthesis. Motivated by the observation that a 3D object should look realistic from multiple viewpoints, these methods introduce a multi-view constraint as regularization to learn valid 3D radiance fields from 2D images. Despite the progress, they often fall short of capturing accurate 3D shapes due to the shape-color ambiguity, limiting their applicability in downstream tasks. In this work, we address this ambiguity by proposing a novel shading-guided generative implicit model that is able to learn a starkly improved shape representation. Our key insight is that an accurate 3D shape should also yield a realistic rendering under different lighting conditions. This multi-lighting constraint is realized by modeling illumination explicitly and performing shading with various lighting conditions. Gradients are derived by feeding the synthesized images to a discriminator. To compensate for the additional computational burden of calculating surface normals, we further devise an efficient volume rendering strategy via surface tracking, reducing the training and inference time by 24% and 48%, respectively. Our experiments on multiple datasets show that the proposed approach achieves photorealistic 3D-aware image synthesis while capturing accurate underlying 3D shapes. We demonstrate improved performance of our approach on 3D shape reconstruction against existing methods, and show its applicability on image relighting. Our code is available at https://github.com/XingangPan/ShadeGAN.
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XCiT: Cross-Covariance Image Transformers
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https://papers.nips.cc/paper_files/paper/2021/hash/a655fbe4b8d7439994aa37ddad80de56-Abstract.html
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Alaaeldin Ali, Hugo Touvron, Mathilde Caron, Piotr Bojanowski, Matthijs Douze, Armand Joulin, Ivan Laptev, Natalia Neverova, Gabriel Synnaeve, Jakob Verbeek, Herve Jegou
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https://papers.nips.cc/paper_files/paper/2021/hash/a655fbe4b8d7439994aa37ddad80de56-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13154-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a655fbe4b8d7439994aa37ddad80de56-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=kzPtpIpF8o
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https://papers.nips.cc/paper_files/paper/2021/file/a655fbe4b8d7439994aa37ddad80de56-Supplemental.pdf
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Following their success in natural language processing, transformers have recently shown much promise for computer vision. The self-attention operation underlying transformers yields global interactions between all tokens ,i.e. words or image patches, and enables flexible modelling of image data beyond the local interactions of convolutions. This flexibility, however, comes with a quadratic complexity in time and memory, hindering application to long sequences and high-resolution images. We propose a “transposed” version of self-attention that operates across feature channels rather than tokens, where the interactions are based on the cross-covariance matrix between keys and queries. The resulting cross-covariance attention (XCA) has linear complexity in the number of tokens, and allows efficient processing of high-resolution images.Our cross-covariance image transformer (XCiT) is built upon XCA. It combines the accuracy of conventional transformers with the scalability of convolutional architectures. We validate the effectiveness and generality of XCiT by reporting excellent results on multiple vision benchmarks, including image classification and self-supervised feature learning on ImageNet-1k, object detection and instance segmentation on COCO, and semantic segmentation on ADE20k.We will opensource our code and trained models to reproduce the reported results.
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Row-clustering of a Point Process-valued Matrix
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https://papers.nips.cc/paper_files/paper/2021/hash/a6a38989dc7e433f1f42388e7afca318-Abstract.html
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Lihao Yin, Ganggang Xu, Huiyan Sang, Yongtao Guan
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https://papers.nips.cc/paper_files/paper/2021/hash/a6a38989dc7e433f1f42388e7afca318-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13155-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a6a38989dc7e433f1f42388e7afca318-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=YXy_2b5wufe
|
https://papers.nips.cc/paper_files/paper/2021/file/a6a38989dc7e433f1f42388e7afca318-Supplemental.pdf
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Structured point process data harvested from various platforms poses new challenges to the machine learning community. To cluster repeatedly observed marked point processes, we propose a novel mixture model of multi-level marked point processes for identifying potential heterogeneity in the observed data. Specifically, we study a matrix whose entries are marked log-Gaussian Cox processes and cluster rows of such a matrix. An efficient semi-parametric Expectation-Solution (ES) algorithm combined with functional principal component analysis (FPCA) of point processes is proposed for model estimation. The effectiveness of the proposed framework is demonstrated through simulation studies and real data analyses.
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Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information
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https://papers.nips.cc/paper_files/paper/2021/hash/a6d259bfbfa2062843ef543e21d7ec8e-Abstract.html
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Yang Zhang, Ashkan Khakzar, Yawei Li, Azade Farshad, Seong Tae Kim, Nassir Navab
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https://papers.nips.cc/paper_files/paper/2021/hash/a6d259bfbfa2062843ef543e21d7ec8e-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13156-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a6d259bfbfa2062843ef543e21d7ec8e-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=HglgPZAYhcG
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https://papers.nips.cc/paper_files/paper/2021/file/a6d259bfbfa2062843ef543e21d7ec8e-Supplemental.zip
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One principal approach for illuminating a black-box neural network is feature attribution, i.e. identifying the importance of input features for the network’s prediction. The predictive information of features is recently proposed as a proxy for the measure of their importance. So far, the predictive information is only identified for latent features by placing an information bottleneck within the network. We propose a method to identify features with predictive information in the input domain. The method results in fine-grained identification of input features' information and is agnostic to network architecture. The core idea of our method is leveraging a bottleneck on the input that only lets input features associated with predictive latent features pass through. We compare our method with several feature attribution methods using mainstream feature attribution evaluation experiments. The code is publicly available.
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Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints
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https://papers.nips.cc/paper_files/paper/2021/hash/a709909b1ea5c2bee24248203b1728a5-Abstract.html
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Maura Pintor, Fabio Roli, Wieland Brendel, Battista Biggio
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https://papers.nips.cc/paper_files/paper/2021/hash/a709909b1ea5c2bee24248203b1728a5-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13157-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a709909b1ea5c2bee24248203b1728a5-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=jfDaBf8PAE
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https://papers.nips.cc/paper_files/paper/2021/file/a709909b1ea5c2bee24248203b1728a5-Supplemental.pdf
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Evaluating adversarial robustness amounts to finding the minimum perturbation needed to have an input sample misclassified. The inherent complexity of the underlying optimization requires current gradient-based attacks to be carefully tuned, initialized, and possibly executed for many computationally-demanding iterations, even if specialized to a given perturbation model.In this work, we overcome these limitations by proposing a fast minimum-norm (FMN) attack that works with different $\ell_p$-norm perturbation models ($p=0, 1, 2, \infty$), is robust to hyperparameter choices, does not require adversarial starting points, and converges within few lightweight steps. It works by iteratively finding the sample misclassified with maximum confidence within an $\ell_p$-norm constraint of size $\epsilon$, while adapting $\epsilon$ to minimize the distance of the current sample to the decision boundary.Extensive experiments show that FMN significantly outperforms existing $\ell_0$, $\ell_1$, and $\ell_\infty$-norm attacks in terms of perturbation size, convergence speed and computation time, while reporting comparable performances with state-of-the-art $\ell_2$-norm attacks. Our open-source code is available at: https://github.com/pralab/Fast-Minimum-Norm-FMN-Attack.
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Uncertainty Quantification and Deep Ensembles
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https://papers.nips.cc/paper_files/paper/2021/hash/a70dc40477bc2adceef4d2c90f47eb82-Abstract.html
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Rahul Rahaman, alexandre thiery
|
https://papers.nips.cc/paper_files/paper/2021/hash/a70dc40477bc2adceef4d2c90f47eb82-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13158-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a70dc40477bc2adceef4d2c90f47eb82-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=wg_kD_nyAF
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https://papers.nips.cc/paper_files/paper/2021/file/a70dc40477bc2adceef4d2c90f47eb82-Supplemental.pdf
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Deep Learning methods are known to suffer from calibration issues: they typically produce over-confident estimates. These problems are exacerbated in the low data regime. Although the calibration of probabilistic models is well studied, calibrating extremely over-parametrized models in the low-data regime presents unique challenges. We show that deep-ensembles do not necessarily lead to improved calibration properties. In fact, we show that standard ensembling methods, when used in conjunction with modern techniques such as mixup regularization, can lead to less calibrated models. This text examines the interplay between three of the most simple and commonly used approaches to leverage deep learning when data is scarce: data-augmentation, ensembling, and post-processing calibration methods. We demonstrate that, although standard ensembling techniques certainly help to boost accuracy, the calibration of deep ensembles relies on subtle trade-offs. We also find that calibration methods such as temperature scaling need to be slightly tweaked when used with deep-ensembles and, crucially, need to be executed after the averaging process. Our simulations indicate that, in the low data regime, this simple strategy can halve the Expected Calibration Error (ECE) on a range of benchmark classification problems when compared to standard deep-ensembles.
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Directed Probabilistic Watershed
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https://papers.nips.cc/paper_files/paper/2021/hash/a73d9b34d6f7c322fa3e34c633b1297d-Abstract.html
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Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht
|
https://papers.nips.cc/paper_files/paper/2021/hash/a73d9b34d6f7c322fa3e34c633b1297d-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13159-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a73d9b34d6f7c322fa3e34c633b1297d-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=fzkU-UMKJIv
|
https://papers.nips.cc/paper_files/paper/2021/file/a73d9b34d6f7c322fa3e34c633b1297d-Supplemental.pdf
|
The Probabilistic Watershed is a semi-supervised learning algorithm applied on undirected graphs. Given a set of labeled nodes (seeds), it defines a Gibbs probability distribution over all possible spanning forests disconnecting the seeds. It calculates, for every node, the probability of sampling a forest connecting a certain seed with the considered node. We propose the "Directed Probabilistic Watershed", an extension of the Probabilistic Watershed algorithm to directed graphs. Building on the Probabilistic Watershed, we apply the Matrix Tree Theorem for directed graphs and define a Gibbs probability distribution over all incoming directed forests rooted at the seeds. Similar to the undirected case, this turns out to be equivalent to the Directed Random Walker. Furthermore, we show that in the limit case in which the Gibbs distribution has infinitely low temperature, the labeling of the Directed Probabilistic Watershed is equal to the one induced by the incoming directed forest of minimum cost. Finally, for illustration, we compare the empirical performance of the proposed method with other semi-supervised segmentation methods for directed graphs.
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Laplace Redux - Effortless Bayesian Deep Learning
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https://papers.nips.cc/paper_files/paper/2021/hash/a7c9585703d275249f30a088cebba0ad-Abstract.html
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Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig
|
https://papers.nips.cc/paper_files/paper/2021/hash/a7c9585703d275249f30a088cebba0ad-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13160-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a7c9585703d275249f30a088cebba0ad-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=gDcaUj4Myhn
|
https://papers.nips.cc/paper_files/paper/2021/file/a7c9585703d275249f30a088cebba0ad-Supplemental.pdf
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Bayesian formulations of deep learning have been shown to have compelling theoretical properties and offer practical functional benefits, such as improved predictive uncertainty quantification and model selection. The Laplace approximation (LA) is a classic, and arguably the simplest family of approximations for the intractable posteriors of deep neural networks. Yet, despite its simplicity, the LA is not as popular as alternatives like variational Bayes or deep ensembles. This may be due to assumptions that the LA is expensive due to the involved Hessian computation, that it is difficult to implement, or that it yields inferior results. In this work we show that these are misconceptions: we (i) review the range of variants of the LA including versions with minimal cost overhead; (ii) introduce "laplace", an easy-to-use software library for PyTorch offering user-friendly access to all major flavors of the LA; and (iii) demonstrate through extensive experiments that the LA is competitive with more popular alternatives in terms of performance, while excelling in terms of computational cost. We hope that this work will serve as a catalyst to a wider adoption of the LA in practical deep learning, including in domains where Bayesian approaches are not typically considered at the moment.
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Hessian Eigenspectra of More Realistic Nonlinear Models
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https://papers.nips.cc/paper_files/paper/2021/hash/a7d8ae4569120b5bec12e7b6e9648b86-Abstract.html
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Zhenyu Liao, Michael W. Mahoney
|
https://papers.nips.cc/paper_files/paper/2021/hash/a7d8ae4569120b5bec12e7b6e9648b86-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13161-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a7d8ae4569120b5bec12e7b6e9648b86-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=o-RYNVOlxA8
| null |
Given an optimization problem, the Hessian matrix and its eigenspectrum can be used in many ways, ranging from designing more efficient second-order algorithms to performing model analysis and regression diagnostics. When nonlinear models and non-convex problems are considered, strong simplifying assumptions are often made to make Hessian spectral analysis more tractable.This leads to the question of how relevant the conclusions of such analyses are for realistic nonlinear models. In this paper, we exploit tools from random matrix theory to make a precise characterization of the Hessian eigenspectra for a broad family of nonlinear models that extends the classical generalized linear models, without relying on strong simplifying assumptions used previously. We show that, depending on the data properties, the nonlinear response model, and the loss function, the Hessian can have qualitatively different spectral behaviors: of bounded or unbounded support, with single- or multi-bulk, and with isolated eigenvalues on the left- or right-hand side of the main eigenvalue bulk. By focusing on such a simple but nontrivial model, our analysis takes a step forward to unveil the theoretical origin of many visually striking features observed in more realistic machine learning models.
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Explicable Reward Design for Reinforcement Learning Agents
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https://papers.nips.cc/paper_files/paper/2021/hash/a7f0d2b95c60161b3f3c82f764b1d1c9-Abstract.html
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Rati Devidze, Goran Radanovic, Parameswaran Kamalaruban, Adish Singla
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https://papers.nips.cc/paper_files/paper/2021/hash/a7f0d2b95c60161b3f3c82f764b1d1c9-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13162-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a7f0d2b95c60161b3f3c82f764b1d1c9-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=yw5KKWraUk7
|
https://papers.nips.cc/paper_files/paper/2021/file/a7f0d2b95c60161b3f3c82f764b1d1c9-Supplemental.pdf
|
We study the design of explicable reward functions for a reinforcement learning agent while guaranteeing that an optimal policy induced by the function belongs to a set of target policies. By being explicable, we seek to capture two properties: (a) informativeness so that the rewards speed up the agent's convergence, and (b) sparseness as a proxy for ease of interpretability of the rewards. The key challenge is that higher informativeness typically requires dense rewards for many learning tasks, and existing techniques do not allow one to balance these two properties appropriately. In this paper, we investigate the problem from the perspective of discrete optimization and introduce a novel framework, ExpRD, to design explicable reward functions. ExpRD builds upon an informativeness criterion that captures the (sub-)optimality of target policies at different time horizons in terms of actions taken from any given starting state. We provide a mathematical analysis of ExpRD, and show its connections to existing reward design techniques, including potential-based reward shaping. Experimental results on two navigation tasks demonstrate the effectiveness of ExpRD in designing explicable reward functions.
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A Minimalist Approach to Offline Reinforcement Learning
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https://papers.nips.cc/paper_files/paper/2021/hash/a8166da05c5a094f7dc03724b41886e5-Abstract.html
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Scott Fujimoto, Shixiang (Shane) Gu
|
https://papers.nips.cc/paper_files/paper/2021/hash/a8166da05c5a094f7dc03724b41886e5-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13163-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a8166da05c5a094f7dc03724b41886e5-Paper.pdf
|
https://papers.nips.cchttps://openreview.net/forum?id=Q32U7dzWXpc
|
https://papers.nips.cc/paper_files/paper/2021/file/a8166da05c5a094f7dc03724b41886e5-Supplemental.pdf
|
Offline reinforcement learning (RL) defines the task of learning from a fixed batch of data. Due to errors in value estimation from out-of-distribution actions, most offline RL algorithms take the approach of constraining or regularizing the policy with the actions contained in the dataset. Built on pre-existing RL algorithms, modifications to make an RL algorithm work offline comes at the cost of additional complexity. Offline RL algorithms introduce new hyperparameters and often leverage secondary components such as generative models, while adjusting the underlying RL algorithm. In this paper we aim to make a deep RL algorithm work while making minimal changes. We find that we can match the performance of state-of-the-art offline RL algorithms by simply adding a behavior cloning term to the policy update of an online RL algorithm and normalizing the data. The resulting algorithm is a simple to implement and tune baseline, while more than halving the overall run time by removing the additional computational overheads of previous methods.
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SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition
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https://papers.nips.cc/paper_files/paper/2021/hash/a860a7886d7c7e2a8d3eaac96f76dc0d-Abstract.html
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Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matt Botvinick, Alexander Lerchner, Chris Burgess
|
https://papers.nips.cc/paper_files/paper/2021/hash/a860a7886d7c7e2a8d3eaac96f76dc0d-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13164-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a860a7886d7c7e2a8d3eaac96f76dc0d-Paper.pdf
|
https://papers.nips.cchttps://openreview.net/forum?id=YSzTMntO1KY
|
https://papers.nips.cc/paper_files/paper/2021/file/a860a7886d7c7e2a8d3eaac96f76dc0d-Supplemental.pdf
|
To help agents reason about scenes in terms of their building blocks, we wish to extract the compositional structure of any given scene (in particular, the configuration and characteristics of objects comprising the scene). This problem is especially difficult when scene structure needs to be inferred while also estimating the agent’s location/viewpoint, as the two variables jointly give rise to the agent’s observations. We present an unsupervised variational approach to this problem. Leveraging the shared structure that exists across different scenes, our model learns to infer two sets of latent representations from RGB video input alone: a set of "object" latents, corresponding to the time-invariant, object-level contents of the scene, as well as a set of "frame" latents, corresponding to global time-varying elements such as viewpoint. This factorization of latents allows our model, SIMONe, to represent object attributes in an allocentric manner which does not depend on viewpoint. Moreover, it allows us to disentangle object dynamics and summarize their trajectories as time-abstracted, view-invariant, per-object properties. We demonstrate these capabilities, as well as the model's performance in terms of view synthesis and instance segmentation, across three procedurally generated video datasets.
| null |
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning
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https://papers.nips.cc/paper_files/paper/2021/hash/a87d27f712df362cd22c7a8ef823e987-Abstract.html
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ZHENHUAN YANG, Yunwen Lei, Puyu Wang, Tianbao Yang, Yiming Ying
|
https://papers.nips.cc/paper_files/paper/2021/hash/a87d27f712df362cd22c7a8ef823e987-Abstract.html
|
NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13165-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a87d27f712df362cd22c7a8ef823e987-Paper.pdf
|
https://papers.nips.cchttps://openreview.net/forum?id=paxcakYWwIu
|
https://papers.nips.cc/paper_files/paper/2021/file/a87d27f712df362cd22c7a8ef823e987-Supplemental.pdf
|
Pairwise learning refers to learning tasks where the loss function depends on a pair of instances. It instantiates many important machine learning tasks such as bipartite ranking and metric learning. A popular approach to handle streaming data in pairwise learning is an online gradient descent (OGD) algorithm, where one needs to pair the current instance with a buffering set of previous instances with a sufficiently large size and therefore suffers from a scalability issue. In this paper, we propose simple stochastic and online gradient descent methods for pairwise learning. A notable difference from the existing studies is that we only pair the current instance with the previous one in building a gradient direction, which is efficient in both the storage and computational complexity. We develop novel stability results, optimization, and generalization error bounds for both convex and nonconvex as well as both smooth and nonsmooth problems. We introduce novel techniques to decouple the dependency of models and the previous instance in both the optimization and generalization analysis. Our study resolves an open question on developing meaningful generalization bounds for OGD using a buffering set with a very small fixed size. We also extend our algorithms and stability analysis to develop differentially private SGD algorithms for pairwise learning which significantly improves the existing results.
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User-Level Differentially Private Learning via Correlated Sampling
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https://papers.nips.cc/paper_files/paper/2021/hash/a89cf525e1d9f04d16ce31165e139a4b-Abstract.html
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Badih Ghazi, Ravi Kumar, Pasin Manurangsi
|
https://papers.nips.cc/paper_files/paper/2021/hash/a89cf525e1d9f04d16ce31165e139a4b-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13166-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a89cf525e1d9f04d16ce31165e139a4b-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=PqiCvohYSAx
| null |
Most works in learning with differential privacy (DP) have focused on the setting where each user has a single sample. In this work, we consider the setting where each user holds $m$ samples and the privacy protection is enforced at the level of each user's data. We show that, in this setting, we may learn with a much fewer number of users. Specifically, we show that, as long as each user receives sufficiently many samples, we can learn any privately learnable class via an $(\epsilon, \delta)$-DP algorithm using only $O(\log(1/\delta)/\epsilon)$ users. For $\epsilon$-DP algorithms, we show that we can learn using only $O_{\epsilon}(d)$ users even in the local model, where $d$ is the probabilistic representation dimension. In both cases, we show a nearly-matching lower bound on the number of users required.A crucial component of our results is a generalization of global stability [Bun, Livni, Moran, FOCS 2020] that allows the use of public randomness. Under this relaxed notion, we employ a correlated sampling strategy to show that the global stability can be boosted to be arbitrarily close to one, at a polynomial expense in the number of samples.
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Asynchronous Decentralized Online Learning
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https://papers.nips.cc/paper_files/paper/2021/hash/a8e864d04c95572d1aece099af852d0a-Abstract.html
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Jiyan Jiang, Wenpeng Zhang, Jinjie GU, Wenwu Zhu
|
https://papers.nips.cc/paper_files/paper/2021/hash/a8e864d04c95572d1aece099af852d0a-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13167-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a8e864d04c95572d1aece099af852d0a-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=VNYKJfYvoCq
|
https://papers.nips.cc/paper_files/paper/2021/file/a8e864d04c95572d1aece099af852d0a-Supplemental.pdf
|
Most existing algorithms in decentralized online learning are conducted in the synchronous setting. However, synchronization makes these algorithms suffer from the straggler problem, i.e., fast learners have to wait for slow learners, which significantly reduces such algorithms' overall efficiency. To overcome this problem, we study decentralized online learning in the asynchronous setting, which allows different learners to work at their own pace. We first formulate the framework of Asynchronous Decentralized Online Convex Optimization, which specifies the whole process of asynchronous decentralized online learning using a sophisticated event indexing system. Then we propose the Asynchronous Decentralized Online Gradient-Push (AD-OGP) algorithm, which performs asymmetric gossiping communication and instantaneous model averaging. We further derive a regret bound of AD-OGP, which is a function of the network topology, the levels of processing delays, and the levels of communication delays. Extensive experiments show that AD-OGP runs significantly faster than its synchronous counterpart and also verify the theoretical results.
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Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs
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https://papers.nips.cc/paper_files/paper/2021/hash/a8ecbabae151abacba7dbde04f761c37-Abstract.html
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Raul Astudillo, Daniel Jiang, Maximilian Balandat, Eytan Bakshy, Peter Frazier
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https://papers.nips.cc/paper_files/paper/2021/hash/a8ecbabae151abacba7dbde04f761c37-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13168-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a8ecbabae151abacba7dbde04f761c37-Paper.pdf
|
https://papers.nips.cchttps://openreview.net/forum?id=hUx6pv-lwWJ
|
https://papers.nips.cc/paper_files/paper/2021/file/a8ecbabae151abacba7dbde04f761c37-Supplemental.pdf
|
Bayesian optimization (BO) is a sample-efficient approach to optimizing costly-to-evaluate black-box functions. Most BO methods ignore how evaluation costs may vary over the optimization domain. However, these costs can be highly heterogeneous and are often unknown in advance in many practical settings, such as hyperparameter tuning of machine learning algorithms or physics-based simulation optimization. Moreover, those few existing methods that acknowledge cost heterogeneity do not naturally accommodate a budget constraint on the total evaluation cost. This combination of unknown costs and a budget constraint introduces a new dimension to the exploration-exploitation trade-off, where learning about the cost incurs a cost itself. Existing methods do not reason about the various trade-offs of this problem in a principled way, leading often to poor performance. We formalize this claim by proving that the expected improvement and the expected improvement per unit of cost, arguably the two most widely used acquisition functions in practice, can be arbitrarily inferior with respect to the optimal non-myopic policy. To overcome the shortcomings of existing approaches, we propose the budgeted multi-step expected improvement, a non-myopic acquisition function that generalizes classical expected improvement to the setting of heterogeneous and unknown evaluation costs. We show that our acquisition function outperforms existing methods in a variety of synthetic and real problems.
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Model-Based Domain Generalization
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https://papers.nips.cc/paper_files/paper/2021/hash/a8f12d9486cbcc2fe0cfc5352011ad35-Abstract.html
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Alexander Robey, George J. Pappas, Hamed Hassani
|
https://papers.nips.cc/paper_files/paper/2021/hash/a8f12d9486cbcc2fe0cfc5352011ad35-Abstract.html
|
NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13169-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a8f12d9486cbcc2fe0cfc5352011ad35-Paper.pdf
|
https://papers.nips.cchttps://openreview.net/forum?id=JOxB9h40A-1
|
https://papers.nips.cc/paper_files/paper/2021/file/a8f12d9486cbcc2fe0cfc5352011ad35-Supplemental.pdf
|
Despite remarkable success in a variety of applications, it is well-known that deep learning can fail catastrophically when presented with out-of-distribution data. Toward addressing this challenge, we consider the \emph{domain generalization} problem, wherein predictors are trained using data drawn from a family of related training domains and then evaluated on a distinct and unseen test domain. We show that under a natural model of data generation and a concomitant invariance condition, the domain generalization problem is equivalent to an infinite-dimensional constrained statistical learning problem; this problem forms the basis of our approach, which we call Model-Based Domain Generalization. Due to the inherent challenges in solving constrained optimization problems in deep learning, we exploit nonconvex duality theory to develop unconstrained relaxations of this statistical problem with tight bounds on the duality gap. Based on this theoretical motivation, we propose a novel domain generalization algorithm with convergence guarantees. In our experiments, we report improvements of up to 30% over state-of-the-art domain generalization baselines on several benchmarks including ColoredMNIST, Camelyon17-WILDS, FMoW-WILDS, and PACS.
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$\alpha$-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression
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https://papers.nips.cc/paper_files/paper/2021/hash/a8f15eda80c50adb0e71943adc8015cf-Abstract.html
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JIABO HE, Sarah Erfani, Xingjun Ma, James Bailey, Ying Chi, Xian-Sheng Hua
|
https://papers.nips.cc/paper_files/paper/2021/hash/a8f15eda80c50adb0e71943adc8015cf-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13170-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/a8f15eda80c50adb0e71943adc8015cf-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=rbdKZJxDWWx
| null |
Bounding box (bbox) regression is a fundamental task in computer vision. So far, the most commonly used loss functions for bbox regression are the Intersection over Union (IoU) loss and its variants. In this paper, we generalize existing IoU-based losses to a new family of power IoU losses that have a power IoU term and an additional power regularization term with a single power parameter $\alpha$. We call this new family of losses the $\alpha$-IoU losses and analyze properties such as order preservingness and loss/gradient reweighting. Experiments on multiple object detection benchmarks and models demonstrate that $\alpha$-IoU losses, 1) can surpass existing IoU-based losses by a noticeable performance margin; 2) offer detectors more flexibility in achieving different levels of bbox regression accuracy by modulating $\alpha$; and 3) are more robust to small datasets and noisy bboxes.
| null |
Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient
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https://papers.nips.cc/paper_files/paper/2021/hash/a8fbbd3b11424ce032ba813493d95ad7-Abstract.html
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David Applegate, Mateo Diaz, Oliver Hinder, Haihao Lu, Miles Lubin, Brendan O'Donoghue, Warren Schudy
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https://papers.nips.cc/paper_files/paper/2021/hash/a8fbbd3b11424ce032ba813493d95ad7-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13171-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a8fbbd3b11424ce032ba813493d95ad7-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=_eXwwWOyqT_
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https://papers.nips.cc/paper_files/paper/2021/file/a8fbbd3b11424ce032ba813493d95ad7-Supplemental.pdf
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We present PDLP, a practical first-order method for linear programming (LP) that can solve to the high levels of accuracy that are expected in traditional LP applications. In addition, it can scale to very large problems because its core operation is matrix-vector multiplications. PDLP is derived by applying the primal-dual hybrid gradient (PDHG) method, popularized by Chambolle and Pock (2011), to a saddle-point formulation of LP. PDLP enhances PDHG for LP by combining several new techniques with older tricks from the literature; the enhancements include diagonal preconditioning, presolving, adaptive step sizes, and adaptive restarting. PDLP improves the state of the art for first-order methods applied to LP. We compare PDLP with SCS, an ADMM-based solver, on a set of 383 LP instances derived from MIPLIB 2017. With a target of $10^{-8}$ relative accuracy and 1 hour time limit, PDLP achieves a 6.3x reduction in the geometric mean of solve times and a 4.6x reduction in the number of instances unsolved (from 227 to 49). Furthermore, we highlight standard benchmark instances and a large-scale application (PageRank) where our open-source prototype of PDLP, written in Julia, outperforms a commercial LP solver.
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On the Provable Generalization of Recurrent Neural Networks
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https://papers.nips.cc/paper_files/paper/2021/hash/a928731e103dfc64c0027fa84709689e-Abstract.html
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Lifu Wang, Bo Shen, Bo Hu, Xing Cao
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https://papers.nips.cc/paper_files/paper/2021/hash/a928731e103dfc64c0027fa84709689e-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13172-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a928731e103dfc64c0027fa84709689e-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=nxrP9J_nG3
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https://papers.nips.cc/paper_files/paper/2021/file/a928731e103dfc64c0027fa84709689e-Supplemental.pdf
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Recurrent Neural Network (RNN) is a fundamental structure in deep learning. Recently, some works study the training process of over-parameterized neural networks, and show that over-parameterized networks can learn functions in some notable concept classes with a provable generalization error bound. In this paper, we analyze the training and generalization for RNNs with random initialization, and provide the following improvements over recent works:(1) For a RNN with input sequence $x=(X_1,X_2,...,X_L)$, previous works study to learn functions that are summation of $f(\beta^T_lX_l)$ and require normalized conditions that $||X_l||\leq\epsilon$ with some very small $\epsilon$ depending on the complexity of $f$. In this paper, using detailed analysis about the neural tangent kernel matrix, we prove a generalization error bound to learn such functions without normalized conditions and show that some notable concept classes are learnable with the numbers of iterations and samples scaling almost-polynomially in the input length $L$.(2) Moreover, we prove a novel result to learn N-variables functions of input sequence with the form $f(\beta^T[X_{l_1},...,X_{l_N}])$, which do not belong to the ``additive'' concept class, i,e., the summation of function $f(X_l)$. And we show that when either $N$ or $l_0=\max(l_1,..,l_N)-\min(l_1,..,l_N)$ is small, $f(\beta^T[X_{l_1},...,X_{l_N}])$ will be learnable with the number iterations and samples scaling almost-polynomially in the input length $L$.
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Differentiable Spline Approximations
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https://papers.nips.cc/paper_files/paper/2021/hash/a952ddeda0b7e2c20744e52e728e5594-Abstract.html
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Minsu Cho, Aditya Balu, Ameya Joshi, Anjana Deva Prasad, Biswajit Khara, Soumik Sarkar, Baskar Ganapathysubramanian, Adarsh Krishnamurthy, Chinmay Hegde
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https://papers.nips.cc/paper_files/paper/2021/hash/a952ddeda0b7e2c20744e52e728e5594-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13173-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a952ddeda0b7e2c20744e52e728e5594-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=jTEGbvLjgp
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https://papers.nips.cc/paper_files/paper/2021/file/a952ddeda0b7e2c20744e52e728e5594-Supplemental.pdf
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The paradigm of differentiable programming has significantly enhanced the scope of machine learning via the judicious use of gradient-based optimization. However, standard differentiable programming methods (such as autodiff) typically require that the machine learning models be differentiable, limiting their applicability. Our goal in this paper is to use a new, principled approach to extend gradient-based optimization to functions well modeled by splines, which encompass a large family of piecewise polynomial models. We derive the form of the (weak) Jacobian of such functions and show that it exhibits a block-sparse structure that can be computed implicitly and efficiently. Overall, we show that leveraging this redesigned Jacobian in the form of a differentiable "layer'' in predictive models leads to improved performance in diverse applications such as image segmentation, 3D point cloud reconstruction, and finite element analysis. We also open-source the code at \url{https://github.com/idealab-isu/DSA}.
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Rate-Optimal Subspace Estimation on Random Graphs
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https://papers.nips.cc/paper_files/paper/2021/hash/a97f6e2fedcabc887911dc9b5fd3ccc3-Abstract.html
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Zhixin Zhou, Fan Zhou, Ping Li, Cun-Hui Zhang
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https://papers.nips.cc/paper_files/paper/2021/hash/a97f6e2fedcabc887911dc9b5fd3ccc3-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13174-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a97f6e2fedcabc887911dc9b5fd3ccc3-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=_KhlwS9oFBp
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https://papers.nips.cc/paper_files/paper/2021/file/a97f6e2fedcabc887911dc9b5fd3ccc3-Supplemental.pdf
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We study the theory of random bipartite graph whose adjacency matrix is generated according to a connectivity matrix $M$. We consider the bipartite graph to be sparse, i.e., the entries of $M$ are upper bounded by certain sparsity parameter. We show that the performance of estimating the connectivity matrix $M$ depends on the sparsity of the graph. We focus on two measurement of performance of estimation: the error of estimating $M$ and the error of estimating the column space of $M$. In the first case, we consider the operator norm and Frobenius norm of the difference between the estimation and the true connectivity matrix. In the second case, the performance will be measured by the difference between the estimated projection matrix and the true projection matrix in operator norm and Frobenius norm. We will show that the estimators we propose achieve the minimax optimal rate.
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Estimating the Unique Information of Continuous Variables
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https://papers.nips.cc/paper_files/paper/2021/hash/a9a1d5317a33ae8cef33961c34144f84-Abstract.html
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Ari Pakman, Amin Nejatbakhsh, Dar Gilboa, Abdullah Makkeh, Luca Mazzucato, Michael Wibral, Elad Schneidman
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https://papers.nips.cc/paper_files/paper/2021/hash/a9a1d5317a33ae8cef33961c34144f84-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13175-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a9a1d5317a33ae8cef33961c34144f84-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=LeW4XOVCrl
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https://papers.nips.cc/paper_files/paper/2021/file/a9a1d5317a33ae8cef33961c34144f84-Supplemental.pdf
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The integration and transfer of information from multiple sources to multiple targets is a core motive of neural systems. The emerging field of partial information decomposition (PID) provides a novel information-theoretic lens into these mechanisms by identifying synergistic, redundant, and unique contributions to the mutual information between one and several variables. While many works have studied aspects of PID for Gaussian and discrete distributions, the case of general continuous distributions is still uncharted territory. In this work we present a method for estimating the unique information in continuous distributions, for the case of one versus two variables. Our method solves the associated optimization problem over the space of distributions with fixed bivariate marginals by combining copula decompositions and techniques developed to optimize variational autoencoders. We obtain excellent agreement with known analytic results for Gaussians, and illustrate the power of our new approach in several brain-inspired neural models. Our method is capable of recovering the effective connectivity of a chaotic network of rate neurons, and uncovers a complex trade-off between redundancy, synergy and unique information in recurrent networks trained to solve a generalized XOR~task.
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Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions
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https://papers.nips.cc/paper_files/paper/2021/hash/a9b4ec2eb4ab7b1b9c3392bb5388119d-Abstract.html
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Ignavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang
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https://papers.nips.cc/paper_files/paper/2021/hash/a9b4ec2eb4ab7b1b9c3392bb5388119d-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13176-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a9b4ec2eb4ab7b1b9c3392bb5388119d-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=R-ZAZ-K1ILb
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https://papers.nips.cc/paper_files/paper/2021/file/a9b4ec2eb4ab7b1b9c3392bb5388119d-Supplemental.pdf
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Many of the causal discovery methods rely on the faithfulness assumption to guarantee asymptotic correctness. However, the assumption can be approximately violated in many ways, leading to sub-optimal solutions. Although there is a line of research in Bayesian network structure learning that focuses on weakening the assumption, such as exact search methods with well-defined score functions, they do not scale well to large graphs. In this work, we introduce several strategies to improve the scalability of exact score-based methods in the linear Gaussian setting. In particular, we develop a super-structure estimation method based on the support of inverse covariance matrix which requires assumptions that are strictly weaker than faithfulness, and apply it to restrict the search space of exact search. We also propose a local search strategy that performs exact search on the local clusters formed by each variable and its neighbors within two hops in the super-structure. Numerical experiments validate the efficacy of the proposed procedure, and demonstrate that it scales up to hundreds of nodes with a high accuracy.
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Node Dependent Local Smoothing for Scalable Graph Learning
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https://papers.nips.cc/paper_files/paper/2021/hash/a9eb812238f753132652ae09963a05e9-Abstract.html
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Wentao Zhang, Mingyu Yang, Zeang Sheng, Yang Li, Wen Ouyang, Yangyu Tao, Zhi Yang, Bin CUI
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https://papers.nips.cc/paper_files/paper/2021/hash/a9eb812238f753132652ae09963a05e9-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13177-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/a9eb812238f753132652ae09963a05e9-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=ekKaTdleJVq
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https://papers.nips.cc/paper_files/paper/2021/file/a9eb812238f753132652ae09963a05e9-Supplemental.pdf
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Recent works reveal that feature or label smoothing lies at the core of Graph Neural Networks (GNNs). Concretely, they show feature smoothing combined with simple linear regression achieves comparable performance with the carefully designed GNNs, and a simple MLP model with label smoothing of its prediction can outperform the vanilla GCN. Though an interesting finding, smoothing has not been well understood, especially regarding how to control the extent of smoothness. Intuitively, too small or too large smoothing iterations may cause under-smoothing or over-smoothing and can lead to sub-optimal performance. Moreover, the extent of smoothness is node-specific, depending on its degree and local structure. To this end, we propose a novel algorithm called node-dependent local smoothing (NDLS), which aims to control the smoothness of every node by setting a node-specific smoothing iteration. Specifically, NDLS computes influence scores based on the adjacency matrix and selects the iteration number by setting a threshold on the scores. Once selected, the iteration number can be applied to both feature smoothing and label smoothing. Experimental results demonstrate that NDLS enjoys high accuracy -- state-of-the-art performance on node classifications tasks, flexibility -- can be incorporated with any models, scalability and efficiency -- can support large scale graphs with fast training.
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Parallel and Efficient Hierarchical k-Median Clustering
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https://papers.nips.cc/paper_files/paper/2021/hash/aa495e18c7e3a21a4e48923b92048a61-Abstract.html
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Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard, Christian Sohler, Ola Svensson
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https://papers.nips.cc/paper_files/paper/2021/hash/aa495e18c7e3a21a4e48923b92048a61-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13178-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/aa495e18c7e3a21a4e48923b92048a61-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=bhdntUKwA1
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https://papers.nips.cc/paper_files/paper/2021/file/aa495e18c7e3a21a4e48923b92048a61-Supplemental.pdf
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As a fundamental unsupervised learning task, hierarchical clustering has been extensively studied in the past decade. In particular, standard metric formulations as hierarchical $k$-center, $k$-means, and $k$-median received a lot of attention and the problems have been studied extensively in different models of computation. Despite all this interest, not many efficient parallel algorithms are known for these problems. In this paper we introduce a new parallel algorithm for the Euclidean hierarchical $k$-median problem that, when using machines with memory $s$ (for $s\in \Omega(\log^2 (n+\Delta+d))$), outputs a hierarchical clustering such that for every fixed value of $k$ the cost of the solution is at most an $O(\min\{d, \log n\} \log \Delta)$ factor larger in expectation than that of an optimal solution. Furthermore, we also get that for all $k$ simultanuously the cost of the solution is at most an $O(\min\{d, \log n\} \log \Delta \log (\Delta d n))$ factor bigger that the corresponding optimal solution. The algorithm requires in $O\left(\log_{s} (nd\log(n+\Delta))\right)$ rounds. Here $d$ is the dimension of the data set and $\Delta$ is the ratio between the maximum and minimum distance of two points in the input dataset. To the best of our knowledge, this is the first \emph{parallel} algorithm for the hierarchical $k$-median problem with theoretical guarantees. We further complement our theoretical results with an empirical study of our algorithm that shows its effectiveness in practice.
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Human-Adversarial Visual Question Answering
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https://papers.nips.cc/paper_files/paper/2021/hash/aa97d584861474f4097cf13ccb5325da-Abstract.html
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Sasha Sheng, Amanpreet Singh, Vedanuj Goswami, Jose Magana, Tristan Thrush, Wojciech Galuba, Devi Parikh, Douwe Kiela
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https://papers.nips.cc/paper_files/paper/2021/hash/aa97d584861474f4097cf13ccb5325da-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13179-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/aa97d584861474f4097cf13ccb5325da-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=vsCCDVdTAx
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https://papers.nips.cc/paper_files/paper/2021/file/aa97d584861474f4097cf13ccb5325da-Supplemental.pdf
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Performance on the most commonly used Visual Question Answering dataset (VQA v2) is starting to approach human accuracy. However, in interacting with state-of-the-art VQA models, it is clear that the problem is far from being solved. In order to stress test VQA models, we benchmark them against human-adversarial examples. Human subjects interact with a state-of-the-art VQA model, and for each image in the dataset, attempt to find a question where the model’s predicted answer is incorrect. We find that a wide range of state-of-the-art models perform poorly when evaluated on these examples. We conduct an extensive analysis of the collected adversarial examples and provide guidance on future research directions. We hope that this Adversarial VQA (AdVQA) benchmark can help drive progress in the field and advance the state of the art.
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Across-animal odor decoding by probabilistic manifold alignment
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https://papers.nips.cc/paper_files/paper/2021/hash/aad64398a969ec3186800d412fa7ab31-Abstract.html
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Pedro Herrero-Vidal, Dmitry Rinberg, Cristina Savin
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https://papers.nips.cc/paper_files/paper/2021/hash/aad64398a969ec3186800d412fa7ab31-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13180-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/aad64398a969ec3186800d412fa7ab31-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=85BzB3WP-qj
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https://papers.nips.cc/paper_files/paper/2021/file/aad64398a969ec3186800d412fa7ab31-Supplemental.zip
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Identifying the common structure of neural dynamics across subjects is key for extracting unifying principles of brain computation and for many brain machine interface applications. Here, we propose a novel probabilistic approach for aligning stimulus-evoked responses from multiple animals in a common low dimensional manifold and use hierarchical inference to identify which stimulus drives neural activity in any given trial. Our probabilistic decoder is robust to a range of features of the neural responses and significantly outperforms existing neural alignment procedures. When applied to recordings from the mouse olfactory bulb, our approach reveals low-dimensional population dynamics that are odor specific and have consistent structure across animals. Thus, our decoder can be used for increasing the robustness and scalability of neural-based chemical detection.
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Excess Capacity and Backdoor Poisoning
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https://papers.nips.cc/paper_files/paper/2021/hash/aaebdb8bb6b0e73f6c3c54a0ab0c6415-Abstract.html
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Naren Manoj, Avrim Blum
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https://papers.nips.cc/paper_files/paper/2021/hash/aaebdb8bb6b0e73f6c3c54a0ab0c6415-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13181-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/aaebdb8bb6b0e73f6c3c54a0ab0c6415-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=8kk8a_zvWua
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https://papers.nips.cc/paper_files/paper/2021/file/aaebdb8bb6b0e73f6c3c54a0ab0c6415-Supplemental.pdf
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A backdoor data poisoning attack is an adversarial attack wherein the attacker injects several watermarked, mislabeled training examples into a training set. The watermark does not impact the test-time performance of the model on typical data; however, the model reliably errs on watermarked examples.To gain a better foundational understanding of backdoor data poisoning attacks, we present a formal theoretical framework within which one can discuss backdoor data poisoning attacks for classification problems. We then use this to analyze important statistical and computational issues surrounding these attacks.On the statistical front, we identify a parameter we call the memorization capacity that captures the intrinsic vulnerability of a learning problem to a backdoor attack. This allows us to argue about the robustness of several natural learning problems to backdoor attacks. Our results favoring the attacker involve presenting explicit constructions of backdoor attacks, and our robustness results show that some natural problem settings cannot yield successful backdoor attacks.From a computational standpoint, we show that under certain assumptions, adversarial training can detect the presence of backdoors in a training set. We then show that under similar assumptions, two closely related problems we call backdoor filtering and robust generalization are nearly equivalent. This implies that it is both asymptotically necessary and sufficient to design algorithms that can identify watermarked examples in the training set in order to obtain a learning algorithm that both generalizes well to unseen data and is robust to backdoors.
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A Convergence Analysis of Gradient Descent on Graph Neural Networks
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https://papers.nips.cc/paper_files/paper/2021/hash/aaf2979785deb27864047e0ea40ef1b7-Abstract.html
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Pranjal Awasthi, Abhimanyu Das, Sreenivas Gollapudi
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https://papers.nips.cc/paper_files/paper/2021/hash/aaf2979785deb27864047e0ea40ef1b7-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13182-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/aaf2979785deb27864047e0ea40ef1b7-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=vCWztO0ppL
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https://papers.nips.cc/paper_files/paper/2021/file/aaf2979785deb27864047e0ea40ef1b7-Supplemental.zip
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Graph Neural Networks~(GNNs) are a powerful class of architectures for solving learning problems on graphs. While many variants of GNNs have been proposed in the literature and have achieved strong empirical performance, their theoretical properties are less well understood. In this work we study the convergence properties of the gradient descent algorithm when used to train GNNs. In particular, we consider the realizable setting where the data is generated from a network with unknown weights and our goal is to study conditions under which gradient descent on a GNN architecture can recover near optimal solutions. While such analysis has been performed in recent years for other architectures such as fully connected feed-forward networks, the message passing nature of the updates in a GNN poses a new challenge in understanding the nature of the gradient descent updates. We take a step towards overcoming this by proving that for the case of deep linear GNNs gradient descent provably recovers solutions up to error $\epsilon$ in $O(\text{log}(1/\epsilon))$ iterations, under natural assumptions on the data distribution. Furthermore, for the case of one-round GNNs with ReLU activations, we show that gradient descent provably recovers solutions up to error $\epsilon$ in $O(\frac{1}{\epsilon^2} \log(\frac{1}{\epsilon}))$ iterations.
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Differentiable rendering with perturbed optimizers
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https://papers.nips.cc/paper_files/paper/2021/hash/ab233b682ec355648e7891e66c54191b-Abstract.html
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Quentin Le Lidec, Ivan Laptev, Cordelia Schmid, Justin Carpentier
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https://papers.nips.cc/paper_files/paper/2021/hash/ab233b682ec355648e7891e66c54191b-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13183-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/ab233b682ec355648e7891e66c54191b-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=EPceRw--ZWr
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https://papers.nips.cc/paper_files/paper/2021/file/ab233b682ec355648e7891e66c54191b-Supplemental.pdf
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Reasoning about 3D scenes from their 2D image projections is one of the core problems in computer vision. Solutions to this inverse and ill-posed problem typically involve a search for models that best explain observed image data. Notably, images depend both on the properties of observed scenes and on the process of image formation. Hence, if optimization techniques should be used to explain images, it is crucial to design differentable functions for the projection of 3D scenes into images, also known as differentiable rendering. Previous approaches to differentiable rendering typically replace non-differentiable operations by smooth approximations, impacting the subsequent 3D estimation. In this paper, we take a more general approach and study differentiable renderers through the prism of randomized optimization and the related notion of perturbed optimizers. In particular, our work highlights the link between some well-known differentiable renderer formulations and randomly smoothed optimizers, and introduces differentiable perturbed renderers. We also propose a variance reduction mechanism to alleviate the computational burden inherent to perturbed optimizers and introduce an adaptive scheme to automatically adjust the smoothing parameters of the rendering process. We apply our method to 3D scene reconstruction and demonstrate its advantages on the tasks of 6D pose estimation and 3D mesh reconstruction. By providing informative gradients that can be used as a strong supervisory signal, we demonstrate the benefits of perturbed renderers to obtain more accurate solutions when compared to the state-of-the-art alternatives using smooth gradient approximations.
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BCORLE($\lambda$): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce Market
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https://papers.nips.cc/paper_files/paper/2021/hash/ab452534c5ce28c4fbb0e102d4a4fb2e-Abstract.html
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Yang Zhang, Bo Tang, Qingyu Yang, Dou An, Hongyin Tang, Chenyang Xi, Xueying LI, Feiyu Xiong
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https://papers.nips.cc/paper_files/paper/2021/hash/ab452534c5ce28c4fbb0e102d4a4fb2e-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13184-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/ab452534c5ce28c4fbb0e102d4a4fb2e-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=yUNQBMsLGA
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Coupons allocation is an important tool for enterprises to increase the activity and loyalty of users on the e-commerce market. One fundamental problem related is how to allocate coupons within a fixed budget while maximizing users' retention on the e-commerce platform. The online e-commerce environment is complicated and ever changing, so it requires the coupons allocation policy learning can quickly adapt to the changes of the company's business strategy. Unfortunately, existing studies with a huge computation overhead can hardly satisfy the requirements of real-time and fast-response in the real world. Specifically, the problem of coupons allocation within a fixed budget is usually formulated as a Lagrangian problem. Existing solutions need to re-learn the policy once the value of Lagrangian multiplier variable $\lambda$ is updated, causing a great computation overhead. Besides, a mature e-commerce market often faces tens of millions of users and dozens of types of coupons which construct the huge policy space, further increasing the difficulty of solving the problem. To tackle with above problems, we propose a budget constrained offline reinforcement learning and evaluation with $\lambda$-generalization (BCORLE($\lambda$)) framework. The proposed method can help enterprises develop a coupons allocation policy which greatly improves users' retention rate on the platform while ensuring the cost does not exceed the budget. Specifically, $\lambda$-generalization method is proposed to lead the policy learning process can be executed according to different $\lambda$ values adaptively, avoiding re-learning new polices from scratch. Thus the computation overhead is greatly reduced. Further, a novel offline reinforcement learning method and an off-policy evaluation algorithm are proposed for policy learning and policy evaluation, respectively. Finally, experiments on the simulation platform and real-world e-commerce market validate the effectiveness of our approach.
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Nested Variational Inference
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https://papers.nips.cc/paper_files/paper/2021/hash/ab49b208848abe14418090d95df0d590-Abstract.html
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Heiko Zimmermann, Hao Wu, Babak Esmaeili, Jan-Willem van de Meent
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https://papers.nips.cc/paper_files/paper/2021/hash/ab49b208848abe14418090d95df0d590-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13185-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/ab49b208848abe14418090d95df0d590-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=i2vd6-7bgBi
| null |
We develop nested variational inference (NVI), a family of methods that learn proposals for nested importance samplers by minimizing an forward or reverse KL divergence at each level of nesting. NVI is applicable to many commonly-used importance sampling strategies and provides a mechanism for learning intermediate densities, which can serve as heuristics to guide the sampler. Our experiments apply NVI to (a) sample from a multimodal distribution using a learned annealing path (b) learn heuristics that approximate the likelihood of future observations in a hidden Markov model and (c) to perform amortized inference in hierarchical deep generative models. We observe that optimizing nested objectives leads to improved sample quality in terms of log average weight and effective sample size.
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Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning
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https://papers.nips.cc/paper_files/paper/2021/hash/ab6439fa2daf0246f92eea433bca5ac4-Abstract.html
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Yingjie Fei, Zhuoran Yang, Yudong Chen, Zhaoran Wang
|
https://papers.nips.cc/paper_files/paper/2021/hash/ab6439fa2daf0246f92eea433bca5ac4-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13186-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/ab6439fa2daf0246f92eea433bca5ac4-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=IUqgofswxo
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https://papers.nips.cc/paper_files/paper/2021/file/ab6439fa2daf0246f92eea433bca5ac4-Supplemental.pdf
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We study risk-sensitive reinforcement learning (RL) based on the entropic risk measure. Although existing works have established non-asymptotic regret guarantees for this problem, they leave open an exponential gap between the upper and lower bounds. We identify the deficiencies in existing algorithms and their analysis that result in such a gap. To remedy these deficiencies, we investigate a simple transformation of the risk-sensitive Bellman equations, which we call the exponential Bellman equation. The exponential Bellman equation inspires us to develop a novel analysis of Bellman backup procedures in risk-sensitive RL algorithms, and further motivates the design of a novel exploration mechanism. We show that these analytic and algorithmic innovations together lead to improved regret upper bounds over existing ones.
| null |
On sensitivity of meta-learning to support data
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https://papers.nips.cc/paper_files/paper/2021/hash/ab73f542b6d60c4de151800b8abc0a6c-Abstract.html
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Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun
|
https://papers.nips.cc/paper_files/paper/2021/hash/ab73f542b6d60c4de151800b8abc0a6c-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13187-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/ab73f542b6d60c4de151800b8abc0a6c-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=Tv0O_cAdKtW
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https://papers.nips.cc/paper_files/paper/2021/file/ab73f542b6d60c4de151800b8abc0a6c-Supplemental.pdf
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Meta-learning algorithms are widely used for few-shot learning. For example, image recognition systems that readily adapt to unseen classes after seeing only a few labeled examples. Despite their success, we show that modern meta-learning algorithms are extremely sensitive to the data used for adaptation, i.e. support data. In particular, we demonstrate the existence of (unaltered, in-distribution, natural) images that, when used for adaptation, yield accuracy as low as 4\% or as high as 95\% on standard few-shot image classification benchmarks. We explain our empirical findings in terms of class margins, which in turn suggests that robust and safe meta-learning requires larger margins than supervised learning.
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On Large-Cohort Training for Federated Learning
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https://papers.nips.cc/paper_files/paper/2021/hash/ab9ebd57177b5106ad7879f0896685d4-Abstract.html
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Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith
|
https://papers.nips.cc/paper_files/paper/2021/hash/ab9ebd57177b5106ad7879f0896685d4-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13188-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/ab9ebd57177b5106ad7879f0896685d4-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=Kb26p7chwhf
|
https://papers.nips.cc/paper_files/paper/2021/file/ab9ebd57177b5106ad7879f0896685d4-Supplemental.pdf
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Federated learning methods typically learn a model by iteratively sampling updates from a population of clients. In this work, we explore how the number of clients sampled at each round (the cohort size) impacts the quality of the learned model and the training dynamics of federated learning algorithms. Our work poses three fundamental questions. First, what challenges arise when trying to scale federated learning to larger cohorts? Second, what parallels exist between cohort sizes in federated learning, and batch sizes in centralized learning? Last, how can we design federated learning methods that effectively utilize larger cohort sizes? We give partial answers to these questions based on extensive empirical evaluation. Our work highlights a number of challenges stemming from the use of larger cohorts. While some of these (such as generalization issues and diminishing returns) are analogs of large-batch training challenges, others (including catastrophic training failures and fairness concerns) are unique to federated learning.
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Generic Neural Architecture Search via Regression
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https://papers.nips.cc/paper_files/paper/2021/hash/aba53da2f6340a8b89dc96d09d0d0430-Abstract.html
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Yuhong Li, Cong Hao, Pan Li, Jinjun Xiong, Deming Chen
|
https://papers.nips.cc/paper_files/paper/2021/hash/aba53da2f6340a8b89dc96d09d0d0430-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13189-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/aba53da2f6340a8b89dc96d09d0d0430-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=mPTfR3Upe0o
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https://papers.nips.cc/paper_files/paper/2021/file/aba53da2f6340a8b89dc96d09d0d0430-Supplemental.pdf
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Most existing neural architecture search (NAS) algorithms are dedicated to and evaluated by the downstream tasks, e.g., image classification in computer vision. However, extensive experiments have shown that, prominent neural architectures, such as ResNet in computer vision and LSTM in natural language processing, are generally good at extracting patterns from the input data and perform well on different downstream tasks. In this paper, we attempt to answer two fundamental questions related to NAS. (1) Is it necessary to use the performance of specific downstream tasks to evaluate and search for good neural architectures? (2) Can we perform NAS effectively and efficiently while being agnostic to the downstream tasks? To answer these questions, we propose a novel and generic NAS framework, termed Generic NAS (GenNAS). GenNAS does not use task-specific labels but instead adopts regression on a set of manually designed synthetic signal bases for architecture evaluation. Such a self-supervised regression task can effectively evaluate the intrinsic power of an architecture to capture and transform the input signal patterns, and allow more sufficient usage of training samples. Extensive experiments across 13 CNN search spaces and one NLP space demonstrate the remarkable efficiency of GenNAS using regression, in terms of both evaluating the neural architectures (quantified by the ranking correlation Spearman's rho between the approximated performances and the downstream task performances) and the convergence speed for training (within a few seconds). For example, on NAS-Bench-101, GenNAS achieves 0.85 rho while the existing efficient methods only achieve 0.38. We then propose an automatic task search to optimize the combination of synthetic signals using limited downstream-task-specific labels, further improving the performance of GenNAS. We also thoroughly evaluate GenNAS's generality and end-to-end NAS performance on all search spaces, which outperforms almost all existing works with significant speedup. For example, on NASBench-201, GenNAS can find near-optimal architectures within 0.3 GPU hour.
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The best of both worlds: stochastic and adversarial episodic MDPs with unknown transition
|
https://papers.nips.cc/paper_files/paper/2021/hash/abb9d15b3293a96a3ea116867b2b16d5-Abstract.html
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Tiancheng Jin, Longbo Huang, Haipeng Luo
|
https://papers.nips.cc/paper_files/paper/2021/hash/abb9d15b3293a96a3ea116867b2b16d5-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13190-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/abb9d15b3293a96a3ea116867b2b16d5-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=-zALR_-372y
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https://papers.nips.cc/paper_files/paper/2021/file/abb9d15b3293a96a3ea116867b2b16d5-Supplemental.pdf
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We consider the best-of-both-worlds problem for learning an episodic Markov Decision Process through $T$ episodes, with the goal of achieving $\widetilde{\mathcal{O}}(\sqrt{T})$ regret when the losses are adversarial and simultaneously $\mathcal{O}(\log T)$ regret when the losses are (almost) stochastic. Recent work by [Jin and Luo, 2020] achieves this goal when the fixed transition is known, and leaves the case of unknown transition as a major open question. In this work, we resolve this open problem by using the same Follow-the-Regularized-Leader (FTRL) framework together with a set of new techniques. Specifically, we first propose a loss-shifting trick in the FTRL analysis, which greatly simplifies the approach of [Jin and Luo, 2020] and already improves their results for the known transition case. Then, we extend this idea to the unknown transition case and develop a novel analysis which upper bounds the transition estimation error by the regret itself in the stochastic setting, a key property to ensure $\mathcal{O}(\log T)$ regret.
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Private learning implies quantum stability
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https://papers.nips.cc/paper_files/paper/2021/hash/abdbeb4d8dbe30df8430a8394b7218ef-Abstract.html
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Yihui Quek, Srinivasan Arunachalam, John A Smolin
|
https://papers.nips.cc/paper_files/paper/2021/hash/abdbeb4d8dbe30df8430a8394b7218ef-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13191-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/abdbeb4d8dbe30df8430a8394b7218ef-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=9XAxGtK5cdN
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https://papers.nips.cc/paper_files/paper/2021/file/abdbeb4d8dbe30df8430a8394b7218ef-Supplemental.pdf
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Learning an unknown n-qubit quantum state rho is a fundamental challenge in quantum computing. Information-theoretically, it is known that tomography requires exponential in n many copies of rho to estimate its entries. Motivated by learning theory, Aaronson et al. introduced many (weaker) learning models: the PAC model of learning states (Proceedings of Royal Society A'07), shadow tomography (STOC'18) for learning shadows" of a state, a model that also requires learners to be differentially private (STOC'19) and the online model of learning states (NeurIPS'18). In these models it was shown that an unknown state can be learnedapproximately" using linear in n many copies of rho. But is there any relationship between these models? In this paper we prove a sequence of (information-theoretic) implications from differentially-private PAC learning to online learning and then to quantum stability.Our main result generalizes the recent work of Bun, Livni and Moran (Journal of the ACM'21) who showed that finite Littlestone dimension (of Boolean-valued concept classes) implies PAC learnability in the (approximate) differentially private (DP) setting. We first consider their work in the real-valued setting and further extend to their techniques to the setting of learning quantum states. Key to our results is our generic quantum online learner, Robust Standard Optimal Algorithm (RSOA), which is robust to adversarial imprecision. We then show information-theoretic implications between DP learning quantum states in the PAC model, learnability of quantum states in the one-way communication model, online learning of quantum states, quantum stability (which is our conceptual contribution), various combinatorial parameters and give further applications to gentle shadow tomography and noisy quantum state learning.
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Interesting Object, Curious Agent: Learning Task-Agnostic Exploration
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https://papers.nips.cc/paper_files/paper/2021/hash/abe8e03e3ac71c2ec3bfb0de042638d8-Abstract.html
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Simone Parisi, Victoria Dean, Deepak Pathak, Abhinav Gupta
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https://papers.nips.cc/paper_files/paper/2021/hash/abe8e03e3ac71c2ec3bfb0de042638d8-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13192-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/abe8e03e3ac71c2ec3bfb0de042638d8-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=knKJgksd7kA
|
https://papers.nips.cc/paper_files/paper/2021/file/abe8e03e3ac71c2ec3bfb0de042638d8-Supplemental.pdf
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Common approaches for task-agnostic exploration learn tabula-rasa --the agent assumes isolated environments and no prior knowledge or experience. However, in the real world, agents learn in many environments and always come with prior experiences as they explore new ones. Exploration is a lifelong process. In this paper, we propose a paradigm change in the formulation and evaluation of task-agnostic exploration. In this setup, the agent first learns to explore across many environments without any extrinsic goal in a task-agnostic manner.Later on, the agent effectively transfers the learned exploration policy to better explore new environments when solving tasks. In this context, we evaluate several baseline exploration strategies and present a simple yet effective approach to learning task-agnostic exploration policies. Our key idea is that there are two components of exploration: (1) an agent-centric component encouraging exploration of unseen parts of the environment based on an agent’s belief; (2) an environment-centric component encouraging exploration of inherently interesting objects. We show that our formulation is effective and provides the most consistent exploration across several training-testing environment pairs. We also introduce benchmarks and metrics for evaluating task-agnostic exploration strategies. The source code is available at https://github.com/sparisi/cbet/.
| null |
SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity Measurement
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https://papers.nips.cc/paper_files/paper/2021/hash/abea47ba24142ed16b7d8fbf2c740e0d-Abstract.html
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Heyang Qin, Samyam Rajbhandari, Olatunji Ruwase, Feng Yan, Lei Yang, Yuxiong He
|
https://papers.nips.cc/paper_files/paper/2021/hash/abea47ba24142ed16b7d8fbf2c740e0d-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13193-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/abea47ba24142ed16b7d8fbf2c740e0d-Paper.pdf
|
https://papers.nips.cchttps://openreview.net/forum?id=ZhF3IVPz-l5
| null |
Large scale training requires massive parallelism to finish the training within a reasonable amount of time. To support massive parallelism, large batch training is the key enabler but often at the cost of generalization performance. Existing works explore adaptive batching or hand-tuned static large batching, in order to strike a balance between the computational efficiency and the performance. However, these methods can provide only coarse-grained adaption (e.g., at a epoch level) due to the intrinsic expensive calculation or hand tuning requirements. In this paper, we propose a fully automated and lightweight adaptive batching methodology to enable fine-grained batch size adaption (e.g., at a mini-batch level) that can achieve state-of-the-art performance with record breaking batch sizes. The core component of our method is a lightweight yet efficient representation of the critical gradient noise information. We open-source the proposed methodology by providing a plugin tool that supports mainstream machine learning frameworks. Extensive evaluations on popular benchmarks (e.g., CIFAR10, ImageNet, and BERT-Large) demonstrate that the proposed methodology outperforms state-of-the-art methodologies using adaptive batching approaches or hand-tuned static strategies in both performance and batch size. Particularly, we achieve a new state-of-the-art batch size of 78k in BERT-Large pretraining with SQuAD score 90.69 compared to 90.58 reported in previous state-of-the-art with 59k batch size.
| null |
Variational Inference for Continuous-Time Switching Dynamical Systems
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https://papers.nips.cc/paper_files/paper/2021/hash/abec16f483abb4f1810ca029aadf8446-Abstract.html
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Lukas Köhs, Bastian Alt, Heinz Koeppl
|
https://papers.nips.cc/paper_files/paper/2021/hash/abec16f483abb4f1810ca029aadf8446-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13194-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/abec16f483abb4f1810ca029aadf8446-Paper.pdf
|
https://papers.nips.cchttps://openreview.net/forum?id=ake1XpIrDKN
|
https://papers.nips.cc/paper_files/paper/2021/file/abec16f483abb4f1810ca029aadf8446-Supplemental.pdf
|
Switching dynamical systems provide a powerful, interpretable modeling framework for inference in time-series data in, e.g., the natural sciences or engineering applications. Since many areas, such as biology or discrete-event systems, are naturally described in continuous time, we present a model based on a Markov jump process modulating a subordinated diffusion process. We provide the exact evolution equations for the prior and posterior marginal densities, the direct solutions of which are however computationally intractable. Therefore, we develop a new continuous-time variational inference algorithm, combining a Gaussian process approximation on the diffusion level with posterior inference for Markov jump processes. By minimizing the path-wise Kullback-Leibler divergence we obtain (i) Bayesian latent state estimates for arbitrary points on the real axis and (ii) point estimates of unknown system parameters, utilizing variational expectation maximization. We extensively evaluate our algorithm under the model assumption and for real-world examples.
| null |
Implicit Regularization in Matrix Sensing via Mirror Descent
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https://papers.nips.cc/paper_files/paper/2021/hash/abf0931987f2f8eb7a8d26f2c21fe172-Abstract.html
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Fan Wu, Patrick Rebeschini
|
https://papers.nips.cc/paper_files/paper/2021/hash/abf0931987f2f8eb7a8d26f2c21fe172-Abstract.html
|
NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13195-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/abf0931987f2f8eb7a8d26f2c21fe172-Paper.pdf
|
https://papers.nips.cchttps://openreview.net/forum?id=3h1iwXmYVVJ
|
https://papers.nips.cc/paper_files/paper/2021/file/abf0931987f2f8eb7a8d26f2c21fe172-Supplemental.zip
|
We study discrete-time mirror descent applied to the unregularized empirical risk in matrix sensing. In both the general case of rectangular matrices and the particular case of positive semidefinite matrices, a simple potential-based analysis in terms of the Bregman divergence allows us to establish convergence of mirror descent---with different choices of the mirror maps---to a matrix that, among all global minimizers of the empirical risk, minimizes a quantity explicitly related to the nuclear norm, the Frobenius norm, and the von Neumann entropy. In both cases, this characterization implies that mirror descent, a first-order algorithm minimizing the unregularized empirical risk, recovers low-rank matrices under the same set of assumptions that are sufficient to guarantee recovery for nuclear-norm minimization. When the sensing matrices are symmetric and commute, we show that gradient descent with full-rank factorized parametrization is a first-order approximation to mirror descent, in which case we obtain an explicit characterization of the implicit bias of gradient flow as a by-product.
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STORM+: Fully Adaptive SGD with Recursive Momentum for Nonconvex Optimization
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https://papers.nips.cc/paper_files/paper/2021/hash/ac10ff1941c540cd87c107330996f4f6-Abstract.html
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Kfir Levy, Ali Kavis, Volkan Cevher
|
https://papers.nips.cc/paper_files/paper/2021/hash/ac10ff1941c540cd87c107330996f4f6-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13196-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/ac10ff1941c540cd87c107330996f4f6-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=ytke6qKpxtr
|
https://papers.nips.cc/paper_files/paper/2021/file/ac10ff1941c540cd87c107330996f4f6-Supplemental.pdf
|
In this work we investigate stochastic non-convex optimization problems where the objective is an expectation over smooth loss functions, and the goal is to find an approximate stationary point. The most popular approach to handling such problems is variance reduction techniques, which are also known to obtain tight convergence rates, matching the lower bounds in this case. Nevertheless, these techniques require a careful maintenance of anchor points in conjunction with appropriately selected ``mega-batchsizes". This leads to a challenging hyperparameter tuning problem, that weakens their practicality. Recently, [Cutkosky and Orabona, 2019] have shown that one can employ recursive momentum in order to avoid the use of anchor points and large batchsizes, and still obtain the optimal rate for this setting. Yet, their method called $\rm{STORM}$ crucially relies on the knowledge of the smoothness, as well a bound on the gradient norms. In this work we propose $\rm{STORM}^{+}$, a new method that is completely parameter-free, does not require large batch-sizes, and obtains the optimal $O(1/T^{1/3})$ rate for finding an approximate stationary point. Our work builds on the $\rm{STORM}$ algorithm, in conjunction with a novel approach to adaptively set the learning rate and momentum parameters.
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Skipping the Frame-Level: Event-Based Piano Transcription With Neural Semi-CRFs
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https://papers.nips.cc/paper_files/paper/2021/hash/ac53fab47b547a0d47b77e424cf119ba-Abstract.html
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Yujia Yan, Frank Cwitkowitz, Zhiyao Duan
|
https://papers.nips.cc/paper_files/paper/2021/hash/ac53fab47b547a0d47b77e424cf119ba-Abstract.html
|
NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13197-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/ac53fab47b547a0d47b77e424cf119ba-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=AzB2Pq7UFsA
| null |
Piano transcription systems are typically optimized to estimate pitch activity at each frame of audio. They are often followed by carefully designed heuristics and post-processing algorithms to estimate note events from the frame-level predictions. Recent methods have also framed piano transcription as a multi-task learning problem, where the activation of different stages of a note event are estimated independently. These practices are not well aligned with the desired outcome of the task, which is the specification of note intervals as holistic events, rather than the aggregation of disjoint observations. In this work, we propose a novel formulation of piano transcription, which is optimized to directly predict note events. Our method is based on Semi-Markov Conditional Random Fields (semi-CRF), which produce scores for intervals rather than individual frames. When formulating piano transcription in this way, we eliminate the need to rely on disjoint frame-level estimates for different stages of a note event. We conduct experiments on the MAESTRO dataset and demonstrate that the proposed model surpasses the current state-of-the-art for piano transcription. Our results suggest that the semi-CRF output layer, while still quadratic in complexity, is a simple, fast and well-performing solution for event-based prediction, and may lead to similar success in other areas which currently rely on frame-level estimates.
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Deep Learning on a Data Diet: Finding Important Examples Early in Training
|
https://papers.nips.cc/paper_files/paper/2021/hash/ac56f8fe9eea3e4a365f29f0f1957c55-Abstract.html
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Mansheej Paul, Surya Ganguli, Gintare Karolina Dziugaite
|
https://papers.nips.cc/paper_files/paper/2021/hash/ac56f8fe9eea3e4a365f29f0f1957c55-Abstract.html
|
NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13198-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/ac56f8fe9eea3e4a365f29f0f1957c55-Paper.pdf
|
https://papers.nips.cchttps://openreview.net/forum?id=Uj7pF-D-YvT
|
https://papers.nips.cc/paper_files/paper/2021/file/ac56f8fe9eea3e4a365f29f0f1957c55-Supplemental.pdf
|
Recent success in deep learning has partially been driven by training increasingly overparametrized networks on ever larger datasets. It is therefore natural to ask: how much of the data is superfluous, which examples are important for generalization, and how do we find them? In this work, we make the striking observation that, in standard vision datasets, simple scores averaged over several weight initializations can be used to identify important examples very early in training. We propose two such scores—the Gradient Normed (GraNd) and the Error L2-Norm (EL2N) scores—and demonstrate their efficacy on a range of architectures and datasets by pruning significant fractions of training data without sacrificing test accuracy. In fact, using EL2N scores calculated a few epochs into training, we can prune half of the CIFAR10 training set while slightly improving test accuracy. Furthermore, for a given dataset, EL2N scores from one architecture or hyperparameter configuration generalize to other configurations. Compared to recent work that prunes data by discarding examples that are rarely forgotten over the course of training, our scores use only local information early in training. We also use our scores to detect noisy examples and study training dynamics through the lens of important examples—we investigate how the data distribution shapes the loss surface and identify subspaces of the model’s data representation that are relatively stable over training.
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BNS: Building Network Structures Dynamically for Continual Learning
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https://papers.nips.cc/paper_files/paper/2021/hash/ac64504cc249b070772848642cffe6ff-Abstract.html
|
Qi Qin, Wenpeng Hu, Han Peng, Dongyan Zhao, Bing Liu
|
https://papers.nips.cc/paper_files/paper/2021/hash/ac64504cc249b070772848642cffe6ff-Abstract.html
|
NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13199-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/ac64504cc249b070772848642cffe6ff-Paper.pdf
|
https://papers.nips.cchttps://openreview.net/forum?id=2ybxtABV2Og
|
https://papers.nips.cc/paper_files/paper/2021/file/ac64504cc249b070772848642cffe6ff-Supplemental.pdf
|
Continual learning (CL) of a sequence of tasks is often accompanied with the catastrophic forgetting(CF) problem. Existing research has achieved remarkable results in overcoming CF, especially for task continual learning. However, limited work has been done to achieve another important goal of CL,knowledge transfer.In this paper, we propose a technique (called BNS) to do both. The novelty of BNS is that it dynamically builds a network to learn each new task to overcome CF and to transfer knowledge across tasks at the same time. Experimental results show that when the tasks are different (with little shared knowledge), BNS can already outperform the state-of-the-art baselines. When the tasks are similar and have shared knowledge, BNS outperforms the baselines substantially by a large margin due to its knowledge transfer capability.
| null |
Auditing Black-Box Prediction Models for Data Minimization Compliance
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https://papers.nips.cc/paper_files/paper/2021/hash/ac6b3cce8c74b2e23688c3e45532e2a7-Abstract.html
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Bashir Rastegarpanah, Krishna Gummadi, Mark Crovella
|
https://papers.nips.cc/paper_files/paper/2021/hash/ac6b3cce8c74b2e23688c3e45532e2a7-Abstract.html
|
NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13200-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/ac6b3cce8c74b2e23688c3e45532e2a7-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=Wkq4hKpGxWv
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https://papers.nips.cc/paper_files/paper/2021/file/ac6b3cce8c74b2e23688c3e45532e2a7-Supplemental.pdf
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In this paper, we focus on auditing black-box prediction models for compliance with the GDPR’s data minimization principle. This principle restricts prediction models to use the minimal information that is necessary for performing the task at hand. Given the challenge of the black-box setting, our key idea is to check if each of the prediction model’s input features is individually necessary by assigning it some constant value (i.e., applying a simple imputation) across all prediction instances, and measuring the extent to which the model outcomes would change. We introduce a metric for data minimization that is based on model instability under simple imputations. We extend the applicability of this metric from a finite sample model to a distributional setting by introducing a probabilistic data minimization guarantee, which we derive using a Bayesian approach. Furthermore, we address the auditing problem under a constraint on the number of queries to the prediction system. We formulate the problem of allocating a budget of system queries to feasible simple imputations (for investigating model instability) as a multi-armed bandit framework with probabilistic success metrics. We define two bandit problems for providing a probabilistic data minimization guarantee at a given confidence level: a decision problem given a data minimization level, and a measurement problem given a fixed query budget. We design efficient algorithms for these auditing problems using novel exploration strategies that expand classical bandit strategies. Our experiments with real-world prediction systems show that our auditing algorithms significantly outperform simpler benchmarks in both measurement and decision problems.
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Dueling Bandits with Team Comparisons
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https://papers.nips.cc/paper_files/paper/2021/hash/ac73001b1d44f4925449ce09d9f5d5ca-Abstract.html
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Lee Cohen, Ulrike Schmidt-Kraepelin, Yishay Mansour
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https://papers.nips.cc/paper_files/paper/2021/hash/ac73001b1d44f4925449ce09d9f5d5ca-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13201-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/ac73001b1d44f4925449ce09d9f5d5ca-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=9PexctnBali
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https://papers.nips.cc/paper_files/paper/2021/file/ac73001b1d44f4925449ce09d9f5d5ca-Supplemental.pdf
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We introduce the dueling teams problem, a new online-learning setting in which the learner observes noisy comparisons of disjoint pairs of $k$-sized teams from a universe of $n$ players. The goal of the learner is to minimize the number of duels required to identify, with high probability, a Condorcet winning team, i.e., a team which wins against any other disjoint team (with probability at least $1/2$).Noisy comparisons are linked to a total order on the teams. We formalize our model by building upon the dueling bandits setting (Yue et al. 2012) and provide several algorithms, both for stochastic and deterministic settings. For the stochastic setting, we provide a reduction to the classical dueling bandits setting, yielding an algorithm that identifies a Condorcet winning team within $\mathcal{O}((n + k \log (k)) \frac{\max(\log\log n, \log k)}{\Delta^2})$ duels, where $\Delta$ is a gap parameter. For deterministic feedback, we additionally present a gap-independent algorithm that identifies a Condorcet winning team within $\mathcal{O}(nk\log(k)+k^5)$ duels.
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Meta Internal Learning
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https://papers.nips.cc/paper_files/paper/2021/hash/ac796a52db3f16bbdb6557d3d89d1c5a-Abstract.html
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Raphael Bensadoun, Shir Gur, Tomer Galanti, Lior Wolf
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https://papers.nips.cc/paper_files/paper/2021/hash/ac796a52db3f16bbdb6557d3d89d1c5a-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13202-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/ac796a52db3f16bbdb6557d3d89d1c5a-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=xfskdMFkuTS
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https://papers.nips.cc/paper_files/paper/2021/file/ac796a52db3f16bbdb6557d3d89d1c5a-Supplemental.pdf
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Internal learning for single-image generation is a framework, where a generator is trained to produce novel images based on a single image. Since these models are trained on a single image, they are limited in their scale and application. To overcome these issues, we propose a meta-learning approach that enables training over a collection of images, in order to model the internal statistics of the sample image more effectively.In the presented meta-learning approach, a single-image GAN model is generated given an input image, via a convolutional feedforward hypernetwork $f$. This network is trained over a dataset of images, allowing for feature sharing among different models, and for interpolation in the space of generative models. The generated single-image model contains a hierarchy of multiple generators and discriminators. It is therefore required to train the meta-learner in an adversarial manner, which requires careful design choices that we justify by a theoretical analysis. Our results show that the models obtained are as suitable as single-image GANs for many common image applications, {significantly reduce the training time per image without loss in performance}, and introduce novel capabilities, such as interpolation and feedforward modeling of novel images.
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Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting
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https://papers.nips.cc/paper_files/paper/2021/hash/ac9815bef801f58de83804bce86984ad-Abstract.html
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Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro
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https://papers.nips.cc/paper_files/paper/2021/hash/ac9815bef801f58de83804bce86984ad-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13203-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/ac9815bef801f58de83804bce86984ad-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=FyOhThdDBM
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https://papers.nips.cc/paper_files/paper/2021/file/ac9815bef801f58de83804bce86984ad-Supplemental.zip
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We consider interpolation learning in high-dimensional linear regression with Gaussian data, and prove a generic uniform convergence guarantee on the generalization error of interpolators in an arbitrary hypothesis class in terms of the class’s Gaussian width. Applying the generic bound to Euclidean norm balls recovers the consistency result of Bartlett et al. (2020) for minimum-norm interpolators, and confirms a prediction of Zhou et al. (2020) for near-minimal-norm interpolators in the special case of Gaussian data. We demonstrate the generality of the bound by applying it to the simplex, obtaining a novel consistency result for minimum $\ell_1$-norm interpolators (basis pursuit). Our results show how norm-based generalization bounds can explain and be used to analyze benign overfitting, at least in some settings.
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Adaptive wavelet distillation from neural networks through interpretations
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https://papers.nips.cc/paper_files/paper/2021/hash/acaa23f71f963e96c8847585e71352d6-Abstract.html
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Wooseok Ha, Chandan Singh, Francois Lanusse, Srigokul Upadhyayula, Bin Yu
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https://papers.nips.cc/paper_files/paper/2021/hash/acaa23f71f963e96c8847585e71352d6-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13204-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/acaa23f71f963e96c8847585e71352d6-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=meTWnAamntJ
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https://papers.nips.cc/paper_files/paper/2021/file/acaa23f71f963e96c8847585e71352d6-Supplemental.pdf
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Recent deep-learning models have achieved impressive prediction performance, but often sacrifice interpretability and computational efficiency. Interpretability is crucial in many disciplines, such as science and medicine, where models must be carefully vetted or where interpretation is the goal itself. Moreover, interpretable models are concise and often yield computational efficiency. Here, we propose adaptive wavelet distillation (AWD), a method which aims to distill information from a trained neural network into a wavelet transform. Specifically, AWD penalizes feature attributions of a neural network in the wavelet domain to learn an effective multi-resolution wavelet transform. The resulting model is highly predictive, concise, computationally efficient, and has properties (such as a multi-scale structure) which make it easy to interpret. In close collaboration with domain experts, we showcase how AWD addresses challenges in two real-world settings: cosmological parameter inference and molecular-partner prediction. In both cases, AWD yields a scientifically interpretable and concise model which gives predictive performance better than state-of-the-art neural networks. Moreover, AWD identifies predictive features that are scientifically meaningful in the context of respective domains. All code and models are released in a full-fledged package available on Github.
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Generative Occupancy Fields for 3D Surface-Aware Image Synthesis
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https://papers.nips.cc/paper_files/paper/2021/hash/acab0116c354964a558e65bdd07ff047-Abstract.html
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Xudong XU, Xingang Pan, Dahua Lin, Bo Dai
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https://papers.nips.cc/paper_files/paper/2021/hash/acab0116c354964a558e65bdd07ff047-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13205-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/acab0116c354964a558e65bdd07ff047-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=tHzvH4Rv1Qa
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https://papers.nips.cc/paper_files/paper/2021/file/acab0116c354964a558e65bdd07ff047-Supplemental.pdf
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The advent of generative radiance fields has significantly promoted the development of 3D-aware image synthesis. The cumulative rendering process in radiance fields makes training these generative models much easier since gradients are distributed over the entire volume, but leads to diffused object surfaces. In the meantime, compared to radiance fields occupancy representations could inherently ensure deterministic surfaces. However, if we directly apply occupancy representations to generative models, during training they will only receive sparse gradients located on object surfaces and eventually suffer from the convergence problem. In this paper, we propose Generative Occupancy Fields (GOF), a novel model based on generative radiance fields that can learn compact object surfaces without impeding its training convergence. The key insight of GOF is a dedicated transition from the cumulative rendering in radiance fields to rendering with only the surface points as the learned surface gets more and more accurate. In this way, GOF combines the merits of two representations in a unified framework. In practice, the training-time transition of start from radiance fields and march to occupancy representations is achieved in GOF by gradually shrinking the sampling region in its rendering process from the entire volume to a minimal neighboring region around the surface. Through comprehensive experiments on multiple datasets, we demonstrate that GOF can synthesize high-quality images with 3D consistency and simultaneously learn compact and smooth object surfaces. Our code is available at https://github.com/SheldonTsui/GOF_NeurIPS2021.
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Relaxed Marginal Consistency for Differentially Private Query Answering
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https://papers.nips.cc/paper_files/paper/2021/hash/acb55f9af76808c5fd5522dcdb519fde-Abstract.html
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Ryan McKenna, Siddhant Pradhan, Daniel R. Sheldon, Gerome Miklau
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https://papers.nips.cc/paper_files/paper/2021/hash/acb55f9af76808c5fd5522dcdb519fde-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13206-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/acb55f9af76808c5fd5522dcdb519fde-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=comGUyv5sac
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https://papers.nips.cc/paper_files/paper/2021/file/acb55f9af76808c5fd5522dcdb519fde-Supplemental.pdf
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Many differentially private algorithms for answering database queries involve astep that reconstructs a discrete data distribution from noisy measurements. Thisprovides consistent query answers and reduces error, but often requires space thatgrows exponentially with dimension. PRIVATE-PGM is a recent approach that usesgraphical models to represent the data distribution, with complexity proportional tothat of exact marginal inference in a graphical model with structure determined bythe co-occurrence of variables in the noisy measurements. PRIVATE-PGM is highlyscalable for sparse measurements, but may fail to run in high dimensions with densemeasurements. We overcome the main scalability limitation of PRIVATE-PGMthrough a principled approach that relaxes consistency constraints in the estimationobjective. Our new approach works with many existing private query answeringalgorithms and improves scalability or accuracy with no privacy cost.
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Local policy search with Bayesian optimization
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https://papers.nips.cc/paper_files/paper/2021/hash/ad0f7a25211abc3889cb0f420c85e671-Abstract.html
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Sarah Müller, Alexander von Rohr, Sebastian Trimpe
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https://papers.nips.cc/paper_files/paper/2021/hash/ad0f7a25211abc3889cb0f420c85e671-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13207-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/ad0f7a25211abc3889cb0f420c85e671-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=jgMyg3KkDb
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https://papers.nips.cc/paper_files/paper/2021/file/ad0f7a25211abc3889cb0f420c85e671-Supplemental.pdf
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Reinforcement learning (RL) aims to find an optimal policy by interaction with an environment. Consequently, learning complex behavior requires a vast number of samples, which can be prohibitive in practice. Nevertheless, instead of systematically reasoning and actively choosing informative samples, policy gradients for local search are often obtained from random perturbations. These random samples yield high variance estimates and hence are sub-optimal in terms of sample complexity. Actively selecting informative samples is at the core of Bayesian optimization, which constructs a probabilistic surrogate of the objective from past samples to reason about informative subsequent ones. In this paper, we propose to join both worlds. We develop an algorithm utilizing a probabilistic model of the objective function and its gradient. Based on the model, the algorithm decides where to query a noisy zeroth-order oracle to improve the gradient estimates. The resulting algorithm is a novel type of policy search method, which we compare to existing black-box algorithms. The comparison reveals improved sample complexity and reduced variance in extensive empirical evaluations on synthetic objectives. Further, we highlight the benefits of active sampling on popular RL benchmarks.
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DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks
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https://papers.nips.cc/paper_files/paper/2021/hash/ad68473a64305626a27c32a5408552d7-Abstract.html
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Wei Sun, Aojun Zhou, Sander Stuijk, Rob Wijnhoven, Andrew Oakleigh Nelson, hongsheng Li, Henk Corporaal
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https://papers.nips.cc/paper_files/paper/2021/hash/ad68473a64305626a27c32a5408552d7-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13208-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/ad68473a64305626a27c32a5408552d7-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=IGrC6koW_g
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https://papers.nips.cc/paper_files/paper/2021/file/ad68473a64305626a27c32a5408552d7-Supplemental.pdf
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Neural pruning is a widely-used compression technique for Deep Neural Networks (DNNs). Recent innovations in Hardware Architectures (e.g. Nvidia Ampere Sparse Tensor Core) and N:M fine-grained Sparse Neural Network algorithms (i.e. every M-weights contains N non-zero values) reveal a promising research line of neural pruning. However, the existing N:M algorithms only address the challenge of how to train N:M sparse neural networks in a uniform fashion (i.e. every layer has the same N:M sparsity) and suffer from a significant accuracy drop for high sparsity (i.e. when sparsity > 80\%). To tackle this problem, we present a novel technique -- \textbf{\textit{DominoSearch}} to find mixed N:M sparsity schemes from pre-trained dense deep neural networks to achieve higher accuracy than the uniform-sparsity scheme with equivalent complexity constraints (e.g. model size or FLOPs). For instance, for the same model size with 2.1M parameters (87.5\% sparsity), our layer-wise N:M sparse ResNet18 outperforms its uniform counterpart by 2.1\% top-1 accuracy, on the large-scale ImageNet dataset. For the same computational complexity of 227M FLOPs, our layer-wise sparse ResNet18 outperforms the uniform one by 1.3\% top-1 accuracy. Furthermore, our layer-wise fine-grained N:M sparse ResNet50 achieves 76.7\% top-1 accuracy with 5.0M parameters. {This is competitive to the results achieved by layer-wise unstructured sparsity} that is believed to be the upper-bound of Neural Network pruning with respect to the accuracy-sparsity trade-off. We believe that our work can build a strong baseline for further sparse DNN research and encourage future hardware-algorithm co-design work. Our code and models are publicly available at \url{https://github.com/NM-sparsity/DominoSearch}.
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Techniques for Symbol Grounding with SATNet
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https://papers.nips.cc/paper_files/paper/2021/hash/ad7ed5d47b9baceb12045a929e7e2f66-Abstract.html
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Sever Topan, David Rolnick, Xujie Si
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https://papers.nips.cc/paper_files/paper/2021/hash/ad7ed5d47b9baceb12045a929e7e2f66-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13209-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/ad7ed5d47b9baceb12045a929e7e2f66-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=lZJHxMxUhV_
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https://papers.nips.cc/paper_files/paper/2021/file/ad7ed5d47b9baceb12045a929e7e2f66-Supplemental.pdf
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Many experts argue that the future of artificial intelligence is limited by the field’s ability to integrate symbolic logical reasoning into deep learning architectures. The recently proposed differentiable MAXSAT solver, SATNet, was a breakthrough in its capacity to integrate with a traditional neural network and solve visual reasoning problems. For instance, it can learn the rules of Sudoku purely from image examples. Despite its success, SATNet was shown to succumb to a key challenge in neurosymbolic systems known as the Symbol Grounding Problem: the inability to map visual inputs to symbolic variables without explicit supervision ("label leakage"). In this work, we present a self-supervised pre-training pipeline that enables SATNet to overcome this limitation, thus broadening the class of problems that SATNet architectures can solve to include datasets where no intermediary labels are available at all. We demonstrate that our method allows SATNet to attain full accuracy even with a harder problem setup that prevents any label leakage. We additionally introduce a proofreading method that further improves the performance of SATNet architectures, beating the state-of-the-art on Visual Sudoku.
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Object DGCNN: 3D Object Detection using Dynamic Graphs
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https://papers.nips.cc/paper_files/paper/2021/hash/ade1d98c5ab2997e867b1151a5c5028d-Abstract.html
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Yue Wang, Justin M. Solomon
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https://papers.nips.cc/paper_files/paper/2021/hash/ade1d98c5ab2997e867b1151a5c5028d-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13210-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/ade1d98c5ab2997e867b1151a5c5028d-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=HwGNkx1WcIs
|
https://papers.nips.cc/paper_files/paper/2021/file/ade1d98c5ab2997e867b1151a5c5028d-Supplemental.zip
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3D object detection often involves complicated training and testing pipelines, which require substantial domain knowledge about individual datasets. Inspired by recent non-maximum suppression-free 2D object detection models, we propose a 3D object detection architecture on point clouds. Our method models 3D object detection as message passing on a dynamic graph, generalizing the DGCNN framework to predict a set of objects. In our construction, we remove the necessity of post-processing via object confidence aggregation or non-maximum suppression. To facilitate object detection from sparse point clouds, we also propose a set-to-set distillation approach customized to 3D detection. This approach aligns the outputs of the teacher model and the student model in a permutation-invariant fashion, significantly simplifying knowledge distillation for the 3D detection task. Our method achieves state-of-the-art performance on autonomous driving benchmarks. We also provide abundant analysis of the detection model and distillation framework.
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Safe Policy Optimization with Local Generalized Linear Function Approximations
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https://papers.nips.cc/paper_files/paper/2021/hash/adf7e293599134777339fdc40ddfa818-Abstract.html
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Akifumi Wachi, Yunyue Wei, Yanan Sui
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https://papers.nips.cc/paper_files/paper/2021/hash/adf7e293599134777339fdc40ddfa818-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13211-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/adf7e293599134777339fdc40ddfa818-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=DWvcqoRAQP8
|
https://papers.nips.cc/paper_files/paper/2021/file/adf7e293599134777339fdc40ddfa818-Supplemental.pdf
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Safe exploration is a key to applying reinforcement learning (RL) in safety-critical systems. Existing safe exploration methods guaranteed safety under the assumption of regularity, and it has been difficult to apply them to large-scale real problems. We propose a novel algorithm, SPO-LF, that optimizes an agent's policy while learning the relation between a locally available feature obtained by sensors and environmental reward/safety using generalized linear function approximations. We provide theoretical guarantees on its safety and optimality. We experimentally show that our algorithm is 1) more efficient in terms of sample complexity and computational cost and 2) more applicable to large-scale problems than previous safe RL methods with theoretical guarantees, and 3) comparably sample-efficient and safer compared with existing advanced deep RL methods with safety constraints.
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Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory
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https://papers.nips.cc/paper_files/paper/2021/hash/adf8d7f8c53c8688e63a02bfb3055497-Abstract.html
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Takashi Matsubara, Yuto Miyatake, Takaharu Yaguchi
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https://papers.nips.cc/paper_files/paper/2021/hash/adf8d7f8c53c8688e63a02bfb3055497-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13212-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/adf8d7f8c53c8688e63a02bfb3055497-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=46J_l-cpc1W
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https://papers.nips.cc/paper_files/paper/2021/file/adf8d7f8c53c8688e63a02bfb3055497-Supplemental.pdf
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A neural network model of a differential equation, namely neural ODE, has enabled the learning of continuous-time dynamical systems and probabilistic distributions with high accuracy. The neural ODE uses the same network repeatedly during a numerical integration. The memory consumption of the backpropagation algorithm is proportional to the number of uses times the network size. This is true even if a checkpointing scheme divides the computation graph into sub-graphs. Otherwise, the adjoint method obtains a gradient by a numerical integration backward in time. Although this method consumes memory only for a single network use, it requires high computational cost to suppress numerical errors. This study proposes the symplectic adjoint method, which is an adjoint method solved by a symplectic integrator. The symplectic adjoint method obtains the exact gradient (up to rounding error) with memory proportional to the number of uses plus the network size. The experimental results demonstrate that the symplectic adjoint method consumes much less memory than the naive backpropagation algorithm and checkpointing schemes, performs faster than the adjoint method, and is more robust to rounding errors.
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Exponential Separation between Two Learning Models and Adversarial Robustness
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https://papers.nips.cc/paper_files/paper/2021/hash/ae06fbdc519bddaa88aa1b24bace4500-Abstract.html
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Grzegorz Gluch, Ruediger Urbanke
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https://papers.nips.cc/paper_files/paper/2021/hash/ae06fbdc519bddaa88aa1b24bace4500-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13213-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/ae06fbdc519bddaa88aa1b24bace4500-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=KUx34DsNKAb
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https://papers.nips.cc/paper_files/paper/2021/file/ae06fbdc519bddaa88aa1b24bace4500-Supplemental.pdf
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We prove an exponential separation for the sample/query complexity between the standard PAC-learning model and a version of the Equivalence-Query-learning model. In the PAC model all samples are provided at the beginning of the learning process. In the Equivalence-Query model the samples are acquired through an interaction between a teacher and a learner, where the teacher provides counterexamples to hypotheses given by the learner. It is intuitive that in an interactive setting fewer samples are needed. We make this formal and prove that in order to achieve an error $\epsilon$ {\em exponentially} (in $\epsilon$) fewer samples suffice than what the PAC bound requires. It was shown experimentally by Stutz, Hein, and Schiele that adversarial training with on-manifold adversarial examples aids generalization (compared to standard training). If we think of the adversarial examples as counterexamples to the current hypothesis then our result can be thought of as a theoretical confirmation of those findings. We also discuss how our result relates to adversarial robustness. In the standard adversarial model one restricts the adversary by introducing a norm constraint. An alternative was pioneered by Goldwasser et. al. Rather than restricting the adversary the learner is enhanced. We pursue a third path. We require the adversary to return samples according to the Equivalance-Query model and show that this leads to robustness. Even though our model has its limitations it provides a fresh point of view on adversarial robustness.
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The balancing principle for parameter choice in distance-regularized domain adaptation
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https://papers.nips.cc/paper_files/paper/2021/hash/ae0909a324fb2530e205e52d40266418-Abstract.html
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Werner Zellinger, Natalia Shepeleva, Marius-Constantin Dinu, Hamid Eghbal-zadeh, Hoan Duc Nguyen, Bernhard Nessler, Sergei Pereverzyev, Bernhard A. Moser
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https://papers.nips.cc/paper_files/paper/2021/hash/ae0909a324fb2530e205e52d40266418-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13214-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/ae0909a324fb2530e205e52d40266418-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=TSxWhJRk4oF
|
https://papers.nips.cc/paper_files/paper/2021/file/ae0909a324fb2530e205e52d40266418-Supplemental.pdf
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We address the unsolved algorithm design problem of choosing a justified regularization parameter in unsupervised domain adaptation. This problem is intriguing as no labels are available in the target domain. Our approach starts with the observation that the widely-used method of minimizing the source error, penalized by a distance measure between source and target feature representations, shares characteristics with regularized ill-posed inverse problems. Regularization parameters in inverse problems are optimally chosen by the fundamental principle of balancing approximation and sampling errors. We use this principle to balance learning errors and domain distance in a target error bound. As a result, we obtain a theoretically justified rule for the choice of the regularization parameter. In contrast to the state of the art, our approach allows source and target distributions with disjoint supports. An empirical comparative study on benchmark datasets underpins the performance of our approach.
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Gaussian Kernel Mixture Network for Single Image Defocus Deblurring
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https://papers.nips.cc/paper_files/paper/2021/hash/ae1eaa32d10b6c886981755d579fb4d8-Abstract.html
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Yuhui Quan, Zicong Wu, Hui Ji
|
https://papers.nips.cc/paper_files/paper/2021/hash/ae1eaa32d10b6c886981755d579fb4d8-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13215-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/ae1eaa32d10b6c886981755d579fb4d8-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=kSR-_SVzDR-
|
https://papers.nips.cc/paper_files/paper/2021/file/ae1eaa32d10b6c886981755d579fb4d8-Supplemental.pdf
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Defocus blur is one kind of blur effects often seen in images, which is challenging to remove due to its spatially variant amount. This paper presents an end-to-end deep learning approach for removing defocus blur from a single image, so as to have an all-in-focus image for consequent vision tasks. First, a pixel-wise Gaussian kernel mixture (GKM) model is proposed for representing spatially variant defocus blur kernels in an efficient linear parametric form, with higher accuracy than existing models. Then, a deep neural network called GKMNet is developed by unrolling a fixed-point iteration of the GKM-based deblurring. The GKMNet is built on a lightweight scale-recurrent architecture, with a scale-recurrent attention module for estimating the mixing coefficients in GKM for defocus deblurring. Extensive experiments show that the GKMNet not only noticeably outperforms existing defocus deblurring methods, but also has its advantages in terms of model complexity and computational efficiency.
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Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks
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https://papers.nips.cc/paper_files/paper/2021/hash/ae3539867aaeec609a4260c6feb725f4-Abstract.html
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Frank Schneider, Felix Dangel, Philipp Hennig
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https://papers.nips.cc/paper_files/paper/2021/hash/ae3539867aaeec609a4260c6feb725f4-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13216-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/ae3539867aaeec609a4260c6feb725f4-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=8AFzk19DNvf
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https://papers.nips.cc/paper_files/paper/2021/file/ae3539867aaeec609a4260c6feb725f4-Supplemental.pdf
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When engineers train deep learning models, they are very much "flying blind". Commonly used methods for real-time training diagnostics, such as monitoring the train/test loss, are limited. Assessing a network's training process solely through these performance indicators is akin to debugging software without access to internal states through a debugger. To address this, we present Cockpit, a collection of instruments that enable a closer look into the inner workings of a learning machine, and a more informative and meaningful status report for practitioners. It facilitates the identification of learning phases and failure modes, like ill-chosen hyperparameters. These instruments leverage novel higher-order information about the gradient distribution and curvature, which has only recently become efficiently accessible. We believe that such a debugging tool, which we open-source for PyTorch, is a valuable help in troubleshooting the training process. By revealing new insights, it also more generally contributes to explainability and interpretability of deep nets.
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MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge
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https://papers.nips.cc/paper_files/paper/2021/hash/ae3f4c649fb55c2ee3ef4d1abdb79ce5-Abstract.html
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Geng Yuan, Xiaolong Ma, Wei Niu, Zhengang Li, Zhenglun Kong, Ning Liu, Yifan Gong, Zheng Zhan, Chaoyang He, Qing Jin, Siyue Wang, Minghai Qin, Bin Ren, Yanzhi Wang, Sijia Liu, Xue Lin
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https://papers.nips.cc/paper_files/paper/2021/hash/ae3f4c649fb55c2ee3ef4d1abdb79ce5-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13217-/bibtex
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https://papers.nips.cc/paper_files/paper/2021/file/ae3f4c649fb55c2ee3ef4d1abdb79ce5-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=VJ7u6SbqorK
|
https://papers.nips.cc/paper_files/paper/2021/file/ae3f4c649fb55c2ee3ef4d1abdb79ce5-Supplemental.pdf
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Recently, a new trend of exploring sparsity for accelerating neural network training has emerged, embracing the paradigm of training on the edge. This paper proposes a novel Memory-Economic Sparse Training (MEST) framework targeting for accurate and fast execution on edge devices. The proposed MEST framework consists of enhancements by Elastic Mutation (EM) and Soft Memory Bound (&S) that ensure superior accuracy at high sparsity ratios. Different from the existing works for sparse training, this current work reveals the importance of sparsity schemes on the performance of sparse training in terms of accuracy as well as training speed on real edge devices. On top of that, the paper proposes to employ data efficiency for further acceleration of sparse training. Our results suggest that unforgettable examples can be identified in-situ even during the dynamic exploration of sparsity masks in the sparse training process, and therefore can be removed for further training speedup on edge devices. Comparing with state-of-the-art (SOTA) works on accuracy, our MEST increases Top-1 accuracy significantly on ImageNet when using the same unstructured sparsity scheme. Systematical evaluation on accuracy, training speed, and memory footprint are conducted, where the proposed MEST framework consistently outperforms representative SOTA works. A reviewer strongly against our work based on his false assumptions and misunderstandings. On top of the previous submission, we employ data efficiency for further acceleration of sparse training. And we explore the impact of model sparsity, sparsity schemes, and sparse training algorithms on the number of removable training examples. Our codes are publicly available at: https://github.com/boone891214/MEST.
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Precise characterization of the prior predictive distribution of deep ReLU networks
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https://papers.nips.cc/paper_files/paper/2021/hash/ae4503ec3da32f5e9033604744ec45ae-Abstract.html
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Lorenzo Noci, Gregor Bachmann, Kevin Roth, Sebastian Nowozin, Thomas Hofmann
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https://papers.nips.cc/paper_files/paper/2021/hash/ae4503ec3da32f5e9033604744ec45ae-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13218-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/ae4503ec3da32f5e9033604744ec45ae-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=DTA7Bgrai-Q
|
https://papers.nips.cc/paper_files/paper/2021/file/ae4503ec3da32f5e9033604744ec45ae-Supplemental.pdf
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Recent works on Bayesian neural networks (BNNs) have highlighted the need to better understand the implications of using Gaussian priors in combination with the compositional structure of the network architecture. Similar in spirit to the kind of analysis that has been developed to devise better initialization schemes for neural networks (cf. He- or Xavier initialization), we derive a precise characterization of the prior predictive distribution of finite-width ReLU networks with Gaussian weights.While theoretical results have been obtained for their heavy-tailedness,the full characterization of the prior predictive distribution (i.e. its density, CDF and moments), remained unknown prior to this work. Our analysis, based on the Meijer-G function, allows us to quantify the influence of architectural choices such as the width or depth of the network on the resulting shape of the prior predictive distribution. We also formally connect our results to previous work in the infinite width setting, demonstrating that the moments of the distribution converge to those of a normal log-normal mixture in the infinite depth limit. Finally, our results provide valuable guidance on prior design: for instance, controlling the predictive variance with depth- and width-informed priors on the weights of the network.
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RED : Looking for Redundancies for Data-FreeStructured Compression of Deep Neural Networks
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https://papers.nips.cc/paper_files/paper/2021/hash/ae5e3ce40e0404a45ecacaaf05e5f735-Abstract.html
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Edouard YVINEC, Arnaud Dapogny, Matthieu Cord, Kevin Bailly
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https://papers.nips.cc/paper_files/paper/2021/hash/ae5e3ce40e0404a45ecacaaf05e5f735-Abstract.html
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NIPS 2021
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https://papers.nips.cc/paper_files/paper/13219-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/ae5e3ce40e0404a45ecacaaf05e5f735-Paper.pdf
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https://papers.nips.cchttps://openreview.net/forum?id=SAPEODcpNvf
|
https://papers.nips.cc/paper_files/paper/2021/file/ae5e3ce40e0404a45ecacaaf05e5f735-Supplemental.pdf
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Deep Neural Networks (DNNs) are ubiquitous in today's computer vision landscape, despite involving considerable computational costs. The mainstream approaches for runtime acceleration consist in pruning connections (unstructured pruning) or, better, filters (structured pruning), both often requiring data to retrain the model. In this paper, we present RED, a data-free, unified approach to tackle structured pruning. First, we propose a novel adaptive hashing of the scalar DNN weight distribution densities to increase the number of identical neurons represented by their weight vectors. Second, we prune the network by merging redundant neurons based on their relative similarities, as defined by their distance. Third, we propose a novel uneven depthwise separation technique to further prune convolutional layers. We demonstrate through a large variety of benchmarks that RED largely outperforms other data-free pruning methods, often reaching performance similar to unconstrained, data-driven methods.
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TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks
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https://papers.nips.cc/paper_files/paper/2021/hash/ae78510109d46b0a6eef9820a4ca95d6-Abstract.html
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YU LI, Min LI, Qiuxia LAI, Yannan Liu, Qiang Xu
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https://papers.nips.cc/paper_files/paper/2021/hash/ae78510109d46b0a6eef9820a4ca95d6-Abstract.html
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NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13220-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/ae78510109d46b0a6eef9820a4ca95d6-Paper.pdf
|
https://papers.nips.cchttps://openreview.net/forum?id=W-agFo22-TS
|
https://papers.nips.cc/paper_files/paper/2021/file/ae78510109d46b0a6eef9820a4ca95d6-Supplemental.zip
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Deep learning (DL) systems are notoriously difficult to test and debug due to the lack of correctness proof and the huge test input space to cover. Given the ubiquitous unlabeled test data and high labeling cost, in this paper, we propose a novel test prioritization technique, namely TestRank, which aims at revealing more model failures with less labeling effort. TestRank brings order into the unlabeled test data according to their likelihood of being a failure, i.e., their failure-revealing capabilities. Different from existing solutions, TestRank leverages both intrinsic and contextual attributes of the unlabeled test data when prioritizing them. To be specific, we first build a similarity graph on both unlabeled test samples and labeled samples (e.g., training or previously labeled test samples). Then, we conduct graph-based semi-supervised learning to extract contextual features from the correctness of similar labeled samples. For a particular test instance, the contextual features extracted with the graph neural network and the intrinsic features obtained with the DL model itself are combined to predict its failure-revealing capability. Finally, TestRank prioritizes unlabeled test inputs in descending order of the above probability value. We evaluate TestRank on three popular image classification datasets, and results show that TestRank significantly outperforms existing test prioritization techniques.
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Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
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https://papers.nips.cc/paper_files/paper/2021/hash/ae816a80e4c1c56caa2eb4e1819cbb2f-Abstract.html
|
Derek Lim, Felix Hohne, Xiuyu Li, Sijia Linda Huang, Vaishnavi Gupta, Omkar Bhalerao, Ser Nam Lim
|
https://papers.nips.cc/paper_files/paper/2021/hash/ae816a80e4c1c56caa2eb4e1819cbb2f-Abstract.html
|
NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13221-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/ae816a80e4c1c56caa2eb4e1819cbb2f-Paper.pdf
|
https://papers.nips.cchttps://openreview.net/forum?id=DfGu8WwT0d
|
https://papers.nips.cc/paper_files/paper/2021/file/ae816a80e4c1c56caa2eb4e1819cbb2f-Supplemental.pdf
|
Many widely used datasets for graph machine learning tasks have generally been homophilous, where nodes with similar labels connect to each other. Recently, new Graph Neural Networks (GNNs) have been developed that move beyond the homophily regime; however, their evaluation has often been conducted on small graphs with limited application domains. We collect and introduce diverse non-homophilous datasets from a variety of application areas that have up to 384x more nodes and 1398x more edges than prior datasets. We further show that existing scalable graph learning and graph minibatching techniques lead to performance degradation on these non-homophilous datasets, thus highlighting the need for further work on scalable non-homophilous methods. To address these concerns, we introduce LINKX --- a strong simple method that admits straightforward minibatch training and inference. Extensive experimental results with representative simple methods and GNNs across our proposed datasets show that LINKX achieves state-of-the-art performance for learning on non-homophilous graphs. Our codes and data are available at https://github.com/CUAI/Non-Homophily-Large-Scale.
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Reinforcement Learning based Disease Progression Model for Alzheimer’s Disease
|
https://papers.nips.cc/paper_files/paper/2021/hash/af1c25e88a9e818f809f6b5d18ca02e2-Abstract.html
|
Krishnakant Saboo, Anirudh Choudhary, Yurui Cao, Gregory Worrell, David Jones, Ravishankar Iyer
|
https://papers.nips.cc/paper_files/paper/2021/hash/af1c25e88a9e818f809f6b5d18ca02e2-Abstract.html
|
NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13222-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/af1c25e88a9e818f809f6b5d18ca02e2-Paper.pdf
|
https://papers.nips.cchttps://openreview.net/forum?id=R4NeFnapYQZ
|
https://papers.nips.cc/paper_files/paper/2021/file/af1c25e88a9e818f809f6b5d18ca02e2-Supplemental.pdf
|
We model Alzheimer’s disease (AD) progression by combining differential equations (DEs) and reinforcement learning (RL) with domain knowledge. DEs provide relationships between some, but not all, factors relevant to AD. We assume that the missing relationships must satisfy general criteria about the working of the brain, for e.g., maximizing cognition while minimizing the cost of supporting cognition. This allows us to extract the missing relationships by using RL to optimize an objective (reward) function that captures the above criteria. We use our model consisting of DEs (as a simulator) and the trained RL agent to predict individualized 10-year AD progression using baseline (year 0) features on synthetic and real data. The model was comparable or better at predicting 10-year cognition trajectories than state-of-the-art learning-based models. Our interpretable model demonstrated, and provided insights into, "recovery/compensatory" processes that mitigate the effect of AD, even though those processes were not explicitly encoded in the model. Our framework combines DEs with RL for modelling AD progression and has broad applicability for understanding other neurological disorders.
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Catch-A-Waveform: Learning to Generate Audio from a Single Short Example
|
https://papers.nips.cc/paper_files/paper/2021/hash/af21d0c97db2e27e13572cbf59eb343d-Abstract.html
|
Gal Greshler, Tamar Shaham, Tomer Michaeli
|
https://papers.nips.cc/paper_files/paper/2021/hash/af21d0c97db2e27e13572cbf59eb343d-Abstract.html
|
NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13223-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/af21d0c97db2e27e13572cbf59eb343d-Paper.pdf
|
https://papers.nips.cchttps://openreview.net/forum?id=XmHnJsiqw9s
|
https://papers.nips.cc/paper_files/paper/2021/file/af21d0c97db2e27e13572cbf59eb343d-Supplemental.pdf
|
Models for audio generation are typically trained on hours of recordings. Here, we illustrate that capturing the essence of an audio source is typically possible from as little as a few tens of seconds from a single training signal. Specifically, we present a GAN-based generative model that can be trained on one short audio signal from any domain (e.g. speech, music, etc.) and does not require pre-training or any other form of external supervision. Once trained, our model can generate random samples of arbitrary duration that maintain semantic similarity to the training waveform, yet exhibit new compositions of its audio primitives. This enables a long line of interesting applications, including generating new jazz improvisations or new a-cappella rap variants based on a single short example, producing coherent modifications to famous songs (e.g. adding a new verse to a Beatles song based solely on the original recording), filling-in of missing parts (inpainting), extending the bandwidth of a speech signal (super-resolution), and enhancing old recordings without access to any clean training example. We show that in all cases, no more than 20 seconds of training audio commonly suffice for our model to achieve state-of-the-art results. This is despite its complete lack of prior knowledge about the nature of audio signals in general.
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Explanation-based Data Augmentation for Image Classification
|
https://papers.nips.cc/paper_files/paper/2021/hash/af3b6a54e9e9338abc54258e3406e485-Abstract.html
|
Sandareka Wickramanayake, Wynne Hsu, Mong Li Lee
|
https://papers.nips.cc/paper_files/paper/2021/hash/af3b6a54e9e9338abc54258e3406e485-Abstract.html
|
NIPS 2021
|
https://papers.nips.cc/paper_files/paper/13224-/bibtex
|
https://papers.nips.cc/paper_files/paper/2021/file/af3b6a54e9e9338abc54258e3406e485-Paper.pdf
|
https://papers.nips.cchttps://openreview.net/forum?id=Ydlco-tfIG
|
https://papers.nips.cc/paper_files/paper/2021/file/af3b6a54e9e9338abc54258e3406e485-Supplemental.pdf
|
Existing works have generated explanations for deep neural network decisions to provide insights into model behavior. We observe that these explanations can also be used to identify concepts that caused misclassifications. This allows us to understand the possible limitations of the dataset used to train the model, particularly the under-represented regions in the dataset. This work proposes a framework that utilizes concept-based explanations to automatically augment the dataset with new images that can cover these under-represented regions to improve the model performance. The framework is able to use the explanations generated by both interpretable classifiers and post-hoc explanations from black-box classifiers. Experiment results demonstrate that the proposed approach improves the accuracy of classifiers compared to state-of-the-art augmentation strategies.
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