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PMNN:Physical Model-driven Neural Network for solving time-fractional differential equations | In this paper, an innovative Physical Model-driven Neural Network (PMNN)
method is proposed to solve time-fractional differential equations. It
establishes a temporal iteration scheme based on physical model-driven neural
networks which effectively combines deep neural networks (DNNs) with
interpolation approximation of fractional derivatives. Specifically, once the
fractional differential operator is discretized, DNNs are employed as a bridge
to integrate interpolation approximation techniques with differential
equations. On the basis of this integration, we construct a neural-based
iteration scheme. Subsequently, by training DNNs to learn this temporal
iteration scheme, approximate solutions to the differential equations can be
obtained. The proposed method aims to preserve the intrinsic physical
information within the equations as far as possible. It fully utilizes the
powerful fitting capability of neural networks while maintaining the efficiency
of the difference schemes for fractional differential equations. Moreover, we
validate the efficiency and accuracy of PMNN through several numerical
experiments. | [
"Zhiying Ma",
"Jie Hou",
"Wenhao Zhu",
"Yaxin Peng",
"Ying Li"
] | 2023-10-07 12:43:32 | http://arxiv.org/abs/2310.04788v1 | http://arxiv.org/pdf/2310.04788v1 | 2310.04788v1 |
Optimal Sequential Decision-Making in Geosteering: A Reinforcement Learning Approach | Trajectory adjustment decisions throughout the drilling process, called
geosteering, affect subsequent choices and information gathering, thus
resulting in a coupled sequential decision problem. Previous works on applying
decision optimization methods in geosteering rely on greedy optimization or
Approximate Dynamic Programming (ADP). Either decision optimization method
requires explicit uncertainty and objective function models, making developing
decision optimization methods for complex and realistic geosteering
environments challenging to impossible. We use the Deep Q-Network (DQN) method,
a model-free reinforcement learning (RL) method that learns directly from the
decision environment, to optimize geosteering decisions. The expensive
computations for RL are handled during the offline training stage. Evaluating
DQN needed for real-time decision support takes milliseconds and is faster than
the traditional alternatives. Moreover, for two previously published synthetic
geosteering scenarios, our results show that RL achieves high-quality outcomes
comparable to the quasi-optimal ADP. Yet, the model-free nature of RL means
that by replacing the training environment, we can extend it to problems where
the solution to ADP is prohibitively expensive to compute. This flexibility
will allow applying it to more complex environments and make hybrid versions
trained with real data in the future. | [
"Ressi Bonti Muhammad",
"Sergey Alyaev",
"Reidar Brumer Bratvold"
] | 2023-10-07 10:49:30 | http://arxiv.org/abs/2310.04772v1 | http://arxiv.org/pdf/2310.04772v1 | 2310.04772v1 |
Online Corrupted User Detection and Regret Minimization | In real-world online web systems, multiple users usually arrive sequentially
into the system. For applications like click fraud and fake reviews, some users
can maliciously perform corrupted (disrupted) behaviors to trick the system.
Therefore, it is crucial to design efficient online learning algorithms to
robustly learn from potentially corrupted user behaviors and accurately
identify the corrupted users in an online manner. Existing works propose bandit
algorithms robust to adversarial corruption. However, these algorithms are
designed for a single user, and cannot leverage the implicit social relations
among multiple users for more efficient learning. Moreover, none of them
consider how to detect corrupted users online in the multiple-user scenario. In
this paper, we present an important online learning problem named LOCUD to
learn and utilize unknown user relations from disrupted behaviors to speed up
learning, and identify the corrupted users in an online setting. To robustly
learn and utilize the unknown relations among potentially corrupted users, we
propose a novel bandit algorithm RCLUB-WCU. To detect the corrupted users, we
devise a novel online detection algorithm OCCUD based on RCLUB-WCU's inferred
user relations. We prove a regret upper bound for RCLUB-WCU, which
asymptotically matches the lower bound with respect to $T$ up to logarithmic
factors, and matches the state-of-the-art results in degenerate cases. We also
give a theoretical guarantee for the detection accuracy of OCCUD. With
extensive experiments, our methods achieve superior performance over previous
bandit algorithms and high corrupted user detection accuracy. | [
"Zhiyong Wang",
"Jize Xie",
"Tong Yu",
"Shuai Li",
"John C. S. Lui"
] | 2023-10-07 10:20:26 | http://arxiv.org/abs/2310.04768v2 | http://arxiv.org/pdf/2310.04768v2 | 2310.04768v2 |
Robust Low-Rank Matrix Completion via a New Sparsity-Inducing Regularizer | This paper presents a novel loss function referred to as hybrid
ordinary-Welsch (HOW) and a new sparsity-inducing regularizer associated with
HOW. We theoretically show that the regularizer is quasiconvex and that the
corresponding Moreau envelope is convex. Moreover, the closed-form solution to
its Moreau envelope, namely, the proximity operator, is derived. Compared with
nonconvex regularizers like the lp-norm with 0<p<1 that requires iterations to
find the corresponding proximity operator, the developed regularizer has a
closed-form proximity operator. We apply our regularizer to the robust matrix
completion problem, and develop an efficient algorithm based on the alternating
direction method of multipliers. The convergence of the suggested method is
analyzed and we prove that any generated accumulation point is a stationary
point. Finally, experimental results based on synthetic and real-world datasets
demonstrate that our algorithm is superior to the state-of-the-art methods in
terms of restoration performance. | [
"Zhi-Yong Wang",
"Hing Cheung So",
"Abdelhak M. Zoubir"
] | 2023-10-07 09:47:55 | http://arxiv.org/abs/2310.04762v1 | http://arxiv.org/pdf/2310.04762v1 | 2310.04762v1 |
Unit Commitment Predictor With a Performance Guarantee: A Support Vector Machine Classifier | The system operators usually need to solve large-scale unit commitment
problems within limited time frame for computation. This paper provides a
pragmatic solution, showing how by learning and predicting the on/off
commitment decisions of conventional units, there is a potential for system
operators to warm start their solver and speed up their computation
significantly. For the prediction, we train linear and kernelized support
vector machine classifiers, providing an out-of-sample performance guarantee if
properly regularized, converting to distributionally robust classifiers. For
the unit commitment problem, we solve a mixed-integer second-order cone
problem. Our results based on the IEEE 6-bus and 118-bus test systems show that
the kernelized SVM with proper regularization outperforms other classifiers,
reducing the computational time by a factor of 1.7. In addition, if there is a
tight computational limit, while the unit commitment problem without warm start
is far away from the optimal solution, its warmly started version can be solved
to optimality within the time limit. | [
"Farzaneh Pourahmadi",
"Jalal Kazempour"
] | 2023-10-07 09:35:59 | http://arxiv.org/abs/2310.08601v1 | http://arxiv.org/pdf/2310.08601v1 | 2310.08601v1 |
A New Dataset for End-to-End Sign Language Translation: The Greek Elementary School Dataset | Automatic Sign Language Translation (SLT) is a research avenue of great
societal impact. End-to-End SLT facilitates the interaction of Hard-of-Hearing
(HoH) with hearing people, thus improving their social life and opportunities
for participation in social life. However, research within this frame of
reference is still in its infancy, and current resources are particularly
limited. Existing SLT methods are either of low translation ability or are
trained and evaluated on datasets of restricted vocabulary and questionable
real-world value. A characteristic example is Phoenix2014T benchmark dataset,
which only covers weather forecasts in German Sign Language. To address this
shortage of resources, we introduce a newly constructed collection of 29653
Greek Sign Language video-translation pairs which is based on the official
syllabus of Greek Elementary School. Our dataset covers a wide range of
subjects. We use this novel dataset to train recent state-of-the-art
Transformer-based methods widely used in SLT research. Our results demonstrate
the potential of our introduced dataset to advance SLT research by offering a
favourable balance between usability and real-world value. | [
"Andreas Voskou",
"Konstantinos P. Panousis",
"Harris Partaourides",
"Kyriakos Tolias",
"Sotirios Chatzis"
] | 2023-10-07 09:18:33 | http://arxiv.org/abs/2310.04753v1 | http://arxiv.org/pdf/2310.04753v1 | 2310.04753v1 |
A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning | Real-world datasets are typically imbalanced in the sense that only a few
classes have numerous samples, while many classes are associated with only a
few samples. As a result, a na\"ive ERM learning process will be biased towards
the majority classes, making it difficult to generalize to the minority
classes. To address this issue, one simple but effective approach is to modify
the loss function to emphasize the learning on minority classes, such as
re-weighting the losses or adjusting the logits via class-dependent terms.
However, existing generalization analysis of such losses is still
coarse-grained and fragmented, failing to explain some empirical results. To
bridge this gap, we propose a novel technique named data-dependent contraction
to capture how these modified losses handle different classes. On top of this
technique, a fine-grained generalization bound is established for imbalanced
learning, which helps reveal the mystery of re-weighting and logit-adjustment
in a unified manner. Furthermore, a principled learning algorithm is developed
based on the theoretical insights. Finally, the empirical results on benchmark
datasets not only validate the theoretical results but also demonstrate the
effectiveness of the proposed method. | [
"Zitai Wang",
"Qianqian Xu",
"Zhiyong Yang",
"Yuan He",
"Xiaochun Cao",
"Qingming Huang"
] | 2023-10-07 09:15:08 | http://arxiv.org/abs/2310.04752v1 | http://arxiv.org/pdf/2310.04752v1 | 2310.04752v1 |
DiffNAS: Bootstrapping Diffusion Models by Prompting for Better Architectures | Diffusion models have recently exhibited remarkable performance on synthetic
data. After a diffusion path is selected, a base model, such as UNet, operates
as a denoising autoencoder, primarily predicting noises that need to be
eliminated step by step. Consequently, it is crucial to employ a model that
aligns with the expected budgets to facilitate superior synthetic performance.
In this paper, we meticulously analyze the diffusion model and engineer a base
model search approach, denoted "DiffNAS". Specifically, we leverage GPT-4 as a
supernet to expedite the search, supplemented with a search memory to enhance
the results. Moreover, we employ RFID as a proxy to promptly rank the
experimental outcomes produced by GPT-4. We also adopt a rapid-convergence
training strategy to boost search efficiency. Rigorous experimentation
corroborates that our algorithm can augment the search efficiency by 2 times
under GPT-based scenarios, while also attaining a performance of 2.82 with 0.37
improvement in FID on CIFAR10 relative to the benchmark IDDPM algorithm. | [
"Wenhao Li",
"Xiu Su",
"Shan You",
"Fei Wang",
"Chen Qian",
"Chang Xu"
] | 2023-10-07 09:10:28 | http://arxiv.org/abs/2310.04750v2 | http://arxiv.org/pdf/2310.04750v2 | 2310.04750v2 |
Digital Twin Assisted Deep Reinforcement Learning for Online Optimization of Network Slicing Admission Control | The proliferation of diverse network services in 5G and beyond networks has
led to the emergence of network slicing technologies. Among these, admission
control plays a crucial role in achieving specific optimization goals through
the selective acceptance of service requests. Although Deep Reinforcement
Learning (DRL) forms the foundation in many admission control approaches for
its effectiveness and flexibility, the initial instability of DRL models
hinders their practical deployment in real-world networks. In this work, we
propose a digital twin (DT) assisted DRL solution to address this issue.
Specifically, we first formulate the admission decision-making process as a
semi-Markov decision process, which is subsequently simplified into an
equivalent discrete-time Markov decision process to facilitate the
implementation of DRL methods. The DT is established through supervised
learning and employed to assist the training phase of the DRL model. Extensive
simulations show that the DT-assisted DRL model increased resource utilization
by over 40\% compared to the directly trained state-of-the-art Dueling-DQN and
over 20\% compared to our directly trained DRL model during initial training.
This improvement is achieved while preserving the model's capacity to optimize
the long-term rewards. | [
"Zhenyu Tao",
"Wei Xu",
"Xiaohu You"
] | 2023-10-07 09:09:19 | http://arxiv.org/abs/2310.09299v1 | http://arxiv.org/pdf/2310.09299v1 | 2310.09299v1 |
Parameter Efficient Multi-task Model Fusion with Partial Linearization | Large pre-trained models have enabled significant advances in machine
learning and served as foundation components. Model fusion methods, such as
task arithmetic, have been proven to be powerful and scalable to incorporate
fine-tuned weights from different tasks into a multi-task model. However,
efficiently fine-tuning large pre-trained models on multiple downstream tasks
remains challenging, leading to inefficient multi-task model fusion. In this
work, we propose a novel method to improve multi-task fusion for
parameter-efficient fine-tuning techniques like LoRA fine-tuning. Specifically,
our approach partially linearizes only the adapter modules and applies task
arithmetic over the linearized adapters. This allows us to leverage the the
advantages of model fusion over linearized fine-tuning, while still performing
fine-tuning and inference efficiently. We demonstrate that our partial
linearization technique enables a more effective fusion of multiple tasks into
a single model, outperforming standard adapter tuning and task arithmetic
alone. Experimental results demonstrate the capabilities of our proposed
partial linearization technique to effectively construct unified multi-task
models via the fusion of fine-tuned task vectors. We evaluate performance over
an increasing number of tasks and find that our approach outperforms standard
parameter-efficient fine-tuning techniques. The results highlight the benefits
of partial linearization for scalable and efficient multi-task model fusion. | [
"Anke Tang",
"Li Shen",
"Yong Luo",
"Yibing Zhan",
"Han Hu",
"Bo Du",
"Yixin Chen",
"Dacheng Tao"
] | 2023-10-07 08:55:54 | http://arxiv.org/abs/2310.04742v2 | http://arxiv.org/pdf/2310.04742v2 | 2310.04742v2 |
Balancing stability and plasticity in continual learning: the readout-decomposition of activation change (RDAC) framework | Continual learning (CL) algorithms strive to acquire new knowledge while
preserving prior information. However, this stability-plasticity trade-off
remains a central challenge. This paper introduces a framework that dissects
this trade-off, offering valuable insights into CL algorithms. The
Readout-Decomposition of Activation Change (RDAC) framework first addresses the
stability-plasticity dilemma and its relation to catastrophic forgetting. It
relates learning-induced activation changes in the range of prior readouts to
the degree of stability and changes in the null space to the degree of
plasticity. In deep non-linear networks tackling split-CIFAR-110 tasks, the
framework clarifies the stability-plasticity trade-offs of the popular
regularization algorithms Synaptic intelligence (SI), Elastic-weight
consolidation (EWC), and learning without Forgetting (LwF), and replay-based
algorithms Gradient episodic memory (GEM), and data replay. GEM and data replay
preserved stability and plasticity, while SI, EWC, and LwF traded off
plasticity for stability. The inability of the regularization algorithms to
maintain plasticity was linked to them restricting the change of activations in
the null space of the prior readout. Additionally, for one-hidden-layer linear
neural networks, we derived a gradient decomposition algorithm to restrict
activation change only in the range of the prior readouts, to maintain high
stability while not further sacrificing plasticity. Results demonstrate that
the algorithm maintained stability without significant plasticity loss. The
RDAC framework informs the behavior of existing CL algorithms and paves the way
for novel CL approaches. Finally, it sheds light on the connection between
learning-induced activation/representation changes and the stability-plasticity
dilemma, also offering insights into representational drift in biological
systems. | [
"Daniel Anthes",
"Sushrut Thorat",
"Peter König",
"Tim C. Kietzmann"
] | 2023-10-07 08:54:43 | http://arxiv.org/abs/2310.04741v2 | http://arxiv.org/pdf/2310.04741v2 | 2310.04741v2 |
Task Aware Modulation using Representation Learning: An Approach for Few Shot Learning in Heterogeneous Systems | We present a Task-aware modulation using Representation Learning (TAM-RL)
framework that enhances personalized predictions in few-shot settings for
heterogeneous systems when individual task characteristics are not known.
TAM-RL extracts embeddings representing the actual inherent characteristics of
these entities and uses these characteristics to personalize the predictions
for each entity/task. Using real-world hydrological and flux tower benchmark
data sets, we show that TAM-RL can significantly outperform existing baseline
approaches such as MAML and multi-modal MAML (MMAML) while being much faster
and simpler to train due to less complexity. Specifically, TAM-RL eliminates
the need for sensitive hyper-parameters like inner loop steps and inner loop
learning rate, which are crucial for model convergence in MAML, MMAML. We
further present an empirical evaluation via synthetic data to explore the
impact of heterogeneity amongst the entities on the relative performance of
MAML, MMAML, and TAM-RL. We show that TAM-RL significantly improves predictive
performance for cases where it is possible to learn distinct representations
for different tasks. | [
"Arvind Renganathan",
"Rahul Ghosh",
"Ankush Khandelwal",
"Vipin Kumar"
] | 2023-10-07 07:55:22 | http://arxiv.org/abs/2310.04727v1 | http://arxiv.org/pdf/2310.04727v1 | 2310.04727v1 |
Activate and Reject: Towards Safe Domain Generalization under Category Shift | Albeit the notable performance on in-domain test points, it is non-trivial
for deep neural networks to attain satisfactory accuracy when deploying in the
open world, where novel domains and object classes often occur. In this paper,
we study a practical problem of Domain Generalization under Category Shift
(DGCS), which aims to simultaneously detect unknown-class samples and classify
known-class samples in the target domains. Compared to prior DG works, we face
two new challenges: 1) how to learn the concept of ``unknown'' during training
with only source known-class samples, and 2) how to adapt the source-trained
model to unseen environments for safe model deployment. To this end, we propose
a novel Activate and Reject (ART) framework to reshape the model's decision
boundary to accommodate unknown classes and conduct post hoc modification to
further discriminate known and unknown classes using unlabeled test data.
Specifically, during training, we promote the response to the unknown by
optimizing the unknown probability and then smoothing the overall output to
mitigate the overconfidence issue. At test time, we introduce a step-wise
online adaptation method that predicts the label by virtue of the cross-domain
nearest neighbor and class prototype information without updating the network's
parameters or using threshold-based mechanisms. Experiments reveal that ART
consistently improves the generalization capability of deep networks on
different vision tasks. For image classification, ART improves the H-score by
6.1% on average compared to the previous best method. For object detection and
semantic segmentation, we establish new benchmarks and achieve competitive
performance. | [
"Chaoqi Chen",
"Luyao Tang",
"Leitian Tao",
"Hong-Yu Zhou",
"Yue Huang",
"Xiaoguang Han",
"Yizhou Yu"
] | 2023-10-07 07:53:12 | http://arxiv.org/abs/2310.04724v1 | http://arxiv.org/pdf/2310.04724v1 | 2310.04724v1 |
Subspace Identification for Multi-Source Domain Adaptation | Multi-source domain adaptation (MSDA) methods aim to transfer knowledge from
multiple labeled source domains to an unlabeled target domain. Although current
methods achieve target joint distribution identifiability by enforcing minimal
changes across domains, they often necessitate stringent conditions, such as an
adequate number of domains, monotonic transformation of latent variables, and
invariant label distributions. These requirements are challenging to satisfy in
real-world applications. To mitigate the need for these strict assumptions, we
propose a subspace identification theory that guarantees the disentanglement of
domain-invariant and domain-specific variables under less restrictive
constraints regarding domain numbers and transformation properties, thereby
facilitating domain adaptation by minimizing the impact of domain shifts on
invariant variables. Based on this theory, we develop a Subspace Identification
Guarantee (SIG) model that leverages variational inference. Furthermore, the
SIG model incorporates class-aware conditional alignment to accommodate target
shifts where label distributions change with the domains. Experimental results
demonstrate that our SIG model outperforms existing MSDA techniques on various
benchmark datasets, highlighting its effectiveness in real-world applications. | [
"Zijian Li",
"Ruichu Cai",
"Guangyi Chen",
"Boyang Sun",
"Zhifeng Hao",
"Kun Zhang"
] | 2023-10-07 07:52:59 | http://arxiv.org/abs/2310.04723v1 | http://arxiv.org/pdf/2310.04723v1 | 2310.04723v1 |
Offline Imitation Learning with Variational Counterfactual Reasoning | In offline Imitation Learning (IL), an agent aims to learn an optimal expert
behavior policy without additional online environment interactions. However, in
many real-world scenarios, such as robotics manipulation, the offline dataset
is collected from suboptimal behaviors without rewards. Due to the scarce
expert data, the agents usually suffer from simply memorizing poor trajectories
and are vulnerable to the variations in the environments, lacking the
capability of generalizing to new environments. To effectively remove spurious
features that would otherwise bias the agent and hinder generalization, we
propose a framework named \underline{O}ffline \underline{I}mitation
\underline{L}earning with \underline{C}ounterfactual data
\underline{A}ugmentation (OILCA). In particular, we leverage the identifiable
variational autoencoder to generate \textit{counterfactual} samples. We
theoretically analyze the counterfactual identification and the improvement of
generalization. Moreover, we conduct extensive experiments to demonstrate that
our approach significantly outperforms various baselines on both
\textsc{DeepMind Control Suite} benchmark for in-distribution robustness and
\textsc{CausalWorld} benchmark for out-of-distribution generalization. | [
"Bowei He",
"Zexu Sun",
"Jinxin Liu",
"Shuai Zhang",
"Xu Chen",
"Chen Ma"
] | 2023-10-07 06:52:18 | http://arxiv.org/abs/2310.04706v3 | http://arxiv.org/pdf/2310.04706v3 | 2310.04706v3 |
EdgeFD: An Edge-Friendly Drift-Aware Fault Diagnosis System for Industrial IoT | Recent transfer learning (TL) approaches in industrial intelligent fault
diagnosis (FD) mostly follow the "pre-train and fine-tuning" paradigm to
address data drift, which emerges from variable working conditions. However, we
find that this approach is prone to the phenomenon known as catastrophic
forgetting. Furthermore, performing frequent models fine-tuning on the
resource-constrained edge nodes can be computationally expensive and
unnecessary, given the excellent transferability demonstrated by existing
models. In this work, we propose the Drift-Aware Weight Consolidation (DAWC), a
method optimized for edge deployments, mitigating the challenges posed by
frequent data drift in the industrial Internet of Things (IIoT). DAWC
efficiently manages multiple data drift scenarios, minimizing the need for
constant model fine-tuning on edge devices, thereby conserving computational
resources. By detecting drift using classifier confidence and estimating
parameter importance with the Fisher Information Matrix, a tool that measures
parameter sensitivity in probabilistic models, we introduce a drift detection
module and a continual learning module to gradually equip the FD model with
powerful generalization capabilities. Experimental results demonstrate that our
proposed DAWC achieves superior performance compared to existing techniques
while also ensuring compatibility with edge computing constraints.
Additionally, we have developed a comprehensive diagnosis and visualization
platform. | [
"Chen Jiao",
"Mao Fengjian",
"Lv Zuohong",
"Tang Jianhua"
] | 2023-10-07 06:48:07 | http://arxiv.org/abs/2310.04704v1 | http://arxiv.org/pdf/2310.04704v1 | 2310.04704v1 |
Integrating Contrastive Learning into a Multitask Transformer Model for Effective Domain Adaptation | While speech emotion recognition (SER) research has made significant
progress, achieving generalization across various corpora continues to pose a
problem. We propose a novel domain adaptation technique that embodies a
multitask framework with SER as the primary task, and contrastive learning and
information maximisation loss as auxiliary tasks, underpinned by fine-tuning of
transformers pre-trained on large language models. Empirical results obtained
through experiments on well-established datasets like IEMOCAP and MSP-IMPROV,
illustrate that our proposed model achieves state-of-the-art performance in SER
within cross-corpus scenarios. | [
"Chung-Soo Ahn",
"Jagath C. Rajapakse",
"Rajib Rana"
] | 2023-10-07 06:41:29 | http://arxiv.org/abs/2310.04703v1 | http://arxiv.org/pdf/2310.04703v1 | 2310.04703v1 |
Twin Graph-based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System | Microservice architecture has sprung up over recent years for managing
enterprise applications, due to its ability to independently deploy and scale
services. Despite its benefits, ensuring the reliability and safety of a
microservice system remains highly challenging. Existing anomaly detection
algorithms based on a single data modality (i.e., metrics, logs, or traces)
fail to fully account for the complex correlations and interactions between
different modalities, leading to false negatives and false alarms, whereas
incorporating more data modalities can offer opportunities for further
performance gain. As a fresh attempt, we propose in this paper a
semi-supervised graph-based anomaly detection method, MSTGAD, which seamlessly
integrates all available data modalities via attentive multi-modal learning.
First, we extract and normalize features from the three modalities, and further
integrate them using a graph, namely MST (microservice system twin) graph,
where each node represents a service instance and the edge indicates the
scheduling relationship between different service instances. The MST graph
provides a virtual representation of the status and scheduling relationships
among service instances of a real-world microservice system. Second, we
construct a transformer-based neural network with both spatial and temporal
attention mechanisms to model the inter-correlations between different
modalities and temporal dependencies between the data points. This enables us
to detect anomalies automatically and accurately in real-time. The source code
of MSTGAD is publicly available at
https://github.com/alipay/microservice_system_twin_graph_based_anomaly_detection. | [
"Jun Huang",
"Yang Yang",
"Hang Yu",
"Jianguo Li",
"Xiao Zheng"
] | 2023-10-07 06:28:41 | http://arxiv.org/abs/2310.04701v1 | http://arxiv.org/pdf/2310.04701v1 | 2310.04701v1 |
Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization | Uplift modeling has shown very promising results in online marketing.
However, most existing works are prone to the robustness challenge in some
practical applications. In this paper, we first present a possible explanation
for the above phenomenon. We verify that there is a feature sensitivity problem
in online marketing using different real-world datasets, where the perturbation
of some key features will seriously affect the performance of the uplift model
and even cause the opposite trend. To solve the above problem, we propose a
novel robustness-enhanced uplift modeling framework with adversarial feature
desensitization (RUAD). Specifically, our RUAD can more effectively alleviate
the feature sensitivity of the uplift model through two customized modules,
including a feature selection module with joint multi-label modeling to
identify a key subset from the input features and an adversarial feature
desensitization module using adversarial training and soft interpolation
operations to enhance the robustness of the model against this selected subset
of features. Finally, we conduct extensive experiments on a public dataset and
a real product dataset to verify the effectiveness of our RUAD in online
marketing. In addition, we also demonstrate the robustness of our RUAD to the
feature sensitivity, as well as the compatibility with different uplift models. | [
"Zexu Sun",
"Bowei He",
"Ming Ma",
"Jiakai Tang",
"Yuchen Wang",
"Chen Ma",
"Dugang Liu"
] | 2023-10-07 05:53:56 | http://arxiv.org/abs/2310.04693v2 | http://arxiv.org/pdf/2310.04693v2 | 2310.04693v2 |
Tight Rates in Supervised Outlier Transfer Learning | A critical barrier to learning an accurate decision rule for outlier
detection is the scarcity of outlier data. As such, practitioners often turn to
the use of similar but imperfect outlier data from which they might transfer
information to the target outlier detection task. Despite the recent empirical
success of transfer learning approaches in outlier detection, a fundamental
understanding of when and how knowledge can be transferred from a source to a
target outlier detection task remains elusive. In this work, we adopt the
traditional framework of Neyman-Pearson classification -- which formalizes
supervised outlier detection -- with the added assumption that one has access
to some related but imperfect outlier data. Our main results are as follows:
We first determine the information-theoretic limits of the problem under a
measure of discrepancy that extends some existing notions from traditional
balanced classification; interestingly, unlike in balanced classification,
seemingly very dissimilar sources can provide much information about a target,
thus resulting in fast transfer.
We then show that, in principle, these information-theoretic limits are
achievable by adaptive procedures, i.e., procedures with no a priori
information on the discrepancy between source and target outlier distributions. | [
"Mohammadreza M. Kalan",
"Samory Kpotufe"
] | 2023-10-07 04:14:13 | http://arxiv.org/abs/2310.04686v1 | http://arxiv.org/pdf/2310.04686v1 | 2310.04686v1 |
The Cost of Down-Scaling Language Models: Fact Recall Deteriorates before In-Context Learning | How does scaling the number of parameters in large language models (LLMs)
affect their core capabilities? We study two natural scaling techniques --
weight pruning and simply training a smaller or larger model, which we refer to
as dense scaling -- and their effects on two core capabilities of LLMs: (a)
recalling facts presented during pre-training and (b) processing information
presented in-context during inference. By curating a suite of tasks that help
disentangle these two capabilities, we find a striking difference in how these
two abilities evolve due to scaling. Reducing the model size by more than 30\%
(via either scaling approach) significantly decreases the ability to recall
facts seen in pre-training. Yet, a 60--70\% reduction largely preserves the
various ways the model can process in-context information, ranging from
retrieving answers from a long context to learning parameterized functions from
in-context exemplars. The fact that both dense scaling and weight pruning
exhibit this behavior suggests that scaling model size has an inherently
disparate effect on fact recall and in-context learning. | [
"Tian Jin",
"Nolan Clement",
"Xin Dong",
"Vaishnavh Nagarajan",
"Michael Carbin",
"Jonathan Ragan-Kelley",
"Gintare Karolina Dziugaite"
] | 2023-10-07 03:36:39 | http://arxiv.org/abs/2310.04680v1 | http://arxiv.org/pdf/2310.04680v1 | 2310.04680v1 |
Surgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots | Recent advances in robot-assisted surgery have resulted in progressively more
precise, efficient, and minimally invasive procedures, sparking a new era of
robotic surgical intervention. This enables doctors, in collaborative
interaction with robots, to perform traditional or minimally invasive surgeries
with improved outcomes through smaller incisions. Recent efforts are working
toward making robotic surgery more autonomous which has the potential to reduce
variability of surgical outcomes and reduce complication rates. Deep
reinforcement learning methodologies offer scalable solutions for surgical
automation, but their effectiveness relies on extensive data acquisition due to
the absence of prior knowledge in successfully accomplishing tasks. Due to the
intensive nature of simulated data collection, previous works have focused on
making existing algorithms more efficient. In this work, we focus on making the
simulator more efficient, making training data much more accessible than
previously possible. We introduce Surgical Gym, an open-source high performance
platform for surgical robot learning where both the physics simulation and
reinforcement learning occur directly on the GPU. We demonstrate between
100-5000x faster training times compared with previous surgical learning
platforms. The code is available at:
https://github.com/SamuelSchmidgall/SurgicalGym. | [
"Samuel Schmidgall",
"Axel Krieger",
"Jason Eshraghian"
] | 2023-10-07 03:21:58 | http://arxiv.org/abs/2310.04676v1 | http://arxiv.org/pdf/2310.04676v1 | 2310.04676v1 |
Modeling non-uniform uncertainty in Reaction Prediction via Boosting and Dropout | Reaction prediction has been recognized as a critical task in synthetic
chemistry, where the goal is to predict the outcome of a reaction based on the
given reactants. With the widespread adoption of generative models, the
Variational Autoencoder(VAE) framework has typically been employed to tackle
challenges in reaction prediction, where the reactants are encoded as a
condition for the decoder, which then generates the product. Despite
effectiveness, these conditional VAE (CVAE) models still fail to adequately
account for the inherent uncertainty in reaction prediction, which primarily
stems from the stochastic reaction process. The principal limitations are
twofold. Firstly, in these CVAE models, the prior is independent of the
reactants, leading to a default wide and assumed uniform distribution variance
of the generated product. Secondly, reactants with analogous molecular
representations are presumed to undergo similar electronic transition
processes, thereby producing similar products. This hinders the ability to
model diverse reaction mechanisms effectively. Since the variance in outcomes
is inherently non-uniform, we are thus motivated to develop a framework that
generates reaction products with non-uniform uncertainty. Firstly, we eliminate
the latent variable in previous CVAE models to mitigate uncontrol-label noise.
Instead, we introduce randomness into product generation via boosting to
ensemble diverse models and cover the range of potential outcomes, and through
dropout to secure models with minor variations. Additionally, we design a
ranking method to union the predictions from boosting and dropout, prioritizing
the most plausible products. Experimental results on the largest reaction
prediction benchmark USPTO-MIT show the superior performance of our proposed
method in modeling the non-uniform uncertainty compared to baselines. | [
"Taicheng Guo",
"Changsheng Ma",
"Xiuying Chen",
"Bozhao Nan",
"Kehan Guo",
"Shichao Pei",
"Nitesh V. Chawla",
"Olaf Wiest",
"Xiangliang Zhang"
] | 2023-10-07 03:18:26 | http://arxiv.org/abs/2310.04674v1 | http://arxiv.org/pdf/2310.04674v1 | 2310.04674v1 |
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT | Generative Pre-trained Transformer (GPT) models have achieved remarkable
performance on various natural language processing tasks. However, there has
been limited research on applying similar frameworks to audio tasks. Previously
proposed large language models for audio tasks either lack sufficient
quantitative evaluations, or are limited to tasks for recognizing and
understanding audio content, or significantly underperform existing
state-of-the-art (SOTA) models. In this paper, we propose LauraGPT, a unified
GPT model for audio recognition, understanding, and generation. LauraGPT is a
versatile language model that can process both audio and text inputs and
generate outputs in either modalities. It can perform a wide range of tasks
related to content, semantics, paralinguistics, and audio-signal analysis. Some
of its noteworthy tasks include automatic speech recognition, speech-to-text
translation, text-to-speech synthesis, machine translation, speech enhancement,
automated audio captioning, speech emotion recognition, and spoken language
understanding. To achieve this goal, we use a combination of continuous and
discrete features for audio. We encode input audio into continuous
representations using an audio encoder and decode output audio from discrete
codec codes. We then fine-tune a large decoder-only Transformer-based language
model on multiple audio-to-text, text-to-audio, audio-to-audio, and
text-to-text tasks using a supervised multitask learning approach. Extensive
experiments show that LauraGPT achieves competitive or superior performance
compared to existing SOTA models on various audio processing benchmarks. | [
"Jiaming Wang",
"Zhihao Du",
"Qian Chen",
"Yunfei Chu",
"Zhifu Gao",
"Zerui Li",
"Kai Hu",
"Xiaohuan Zhou",
"Jin Xu",
"Ziyang Ma",
"Wen Wang",
"Siqi Zheng",
"Chang Zhou",
"Zhijie Yan",
"Shiliang Zhang"
] | 2023-10-07 03:17:59 | http://arxiv.org/abs/2310.04673v3 | http://arxiv.org/pdf/2310.04673v3 | 2310.04673v3 |
Label-free Node Classification on Graphs with Large Language Models (LLMS) | In recent years, there have been remarkable advancements in node
classification achieved by Graph Neural Networks (GNNs). However, they
necessitate abundant high-quality labels to ensure promising performance. In
contrast, Large Language Models (LLMs) exhibit impressive zero-shot proficiency
on text-attributed graphs. Yet, they face challenges in efficiently processing
structural data and suffer from high inference costs. In light of these
observations, this work introduces a label-free node classification on graphs
with LLMs pipeline, LLM-GNN. It amalgamates the strengths of both GNNs and LLMs
while mitigating their limitations. Specifically, LLMs are leveraged to
annotate a small portion of nodes and then GNNs are trained on LLMs'
annotations to make predictions for the remaining large portion of nodes. The
implementation of LLM-GNN faces a unique challenge: how can we actively select
nodes for LLMs to annotate and consequently enhance the GNN training? How can
we leverage LLMs to obtain annotations of high quality, representativeness, and
diversity, thereby enhancing GNN performance with less cost? To tackle this
challenge, we develop an annotation quality heuristic and leverage the
confidence scores derived from LLMs to advanced node selection. Comprehensive
experimental results validate the effectiveness of LLM-GNN. In particular,
LLM-GNN can achieve an accuracy of 74.9% on a vast-scale dataset \products with
a cost less than 1 dollar. | [
"Zhikai Chen",
"Haitao Mao",
"Hongzhi Wen",
"Haoyu Han",
"Wei Jin",
"Haiyang Zhang",
"Hui Liu",
"Jiliang Tang"
] | 2023-10-07 03:14:11 | http://arxiv.org/abs/2310.04668v2 | http://arxiv.org/pdf/2310.04668v2 | 2310.04668v2 |
Oracle Efficient Algorithms for Groupwise Regret | We study the problem of online prediction, in which at each time step $t$, an
individual $x_t$ arrives, whose label we must predict. Each individual is
associated with various groups, defined based on their features such as age,
sex, race etc., which may intersect. Our goal is to make predictions that have
regret guarantees not just overall but also simultaneously on each sub-sequence
comprised of the members of any single group. Previous work such as [Blum &
Lykouris] and [Lee et al] provide attractive regret guarantees for these
problems; however, these are computationally intractable on large model
classes. We show that a simple modification of the sleeping experts technique
of [Blum & Lykouris] yields an efficient reduction to the well-understood
problem of obtaining diminishing external regret absent group considerations.
Our approach gives similar regret guarantees compared to [Blum & Lykouris];
however, we run in time linear in the number of groups, and are
oracle-efficient in the hypothesis class. This in particular implies that our
algorithm is efficient whenever the number of groups is polynomially bounded
and the external-regret problem can be solved efficiently, an improvement on
[Blum & Lykouris]'s stronger condition that the model class must be small. Our
approach can handle online linear regression and online combinatorial
optimization problems like online shortest paths. Beyond providing theoretical
regret bounds, we evaluate this algorithm with an extensive set of experiments
on synthetic data and on two real data sets -- Medical costs and the Adult
income dataset, both instantiated with intersecting groups defined in terms of
race, sex, and other demographic characteristics. We find that uniformly across
groups, our algorithm gives substantial error improvements compared to running
a standard online linear regression algorithm with no groupwise regret
guarantees. | [
"Krishna Acharya",
"Eshwar Ram Arunachaleswaran",
"Sampath Kannan",
"Aaron Roth",
"Juba Ziani"
] | 2023-10-07 02:17:22 | http://arxiv.org/abs/2310.04652v1 | http://arxiv.org/pdf/2310.04652v1 | 2310.04652v1 |
NPEFF: Non-Negative Per-Example Fisher Factorization | As deep learning models are deployed in more and more settings, it becomes
increasingly important to be able to understand why they produce a given
prediction, but interpretation of these models remains a challenge. In this
paper, we introduce a novel interpretability method called NPEFF that is
readily applicable to any end-to-end differentiable model. It operates on the
principle that processing of a characteristic shared across different examples
involves a specific subset of model parameters. We perform NPEFF by decomposing
each example's Fisher information matrix as a non-negative sum of components.
These components take the form of either non-negative vectors or rank-1
positive semi-definite matrices depending on whether we are using diagonal or
low-rank Fisher representations, respectively. For the latter form, we
introduce a novel and highly scalable algorithm. We demonstrate that components
recovered by NPEFF have interpretable tunings through experiments on language
and vision models. Using unique properties of NPEFF's parameter-space
representations, we ran extensive experiments to verify that the connections
between directions in parameters space and examples recovered by NPEFF actually
reflect the model's processing. We further demonstrate NPEFF's ability to
uncover the actual processing strategies used by a TRACR-compiled model. We
further explore a potential application of NPEFF in uncovering and correcting
flawed heuristics used by a model. We release our code to facilitate research
using NPEFF. | [
"Michael Matena",
"Colin Raffel"
] | 2023-10-07 02:02:45 | http://arxiv.org/abs/2310.04649v1 | http://arxiv.org/pdf/2310.04649v1 | 2310.04649v1 |
Automatic Anonymization of Swiss Federal Supreme Court Rulings | Releasing court decisions to the public relies on proper anonymization to
protect all involved parties, where necessary. The Swiss Federal Supreme Court
relies on an existing system that combines different traditional computational
methods with human experts. In this work, we enhance the existing anonymization
software using a large dataset annotated with entities to be anonymized. We
compared BERT-based models with models pre-trained on in-domain data. Our
results show that using in-domain data to pre-train the models further improves
the F1-score by more than 5\% compared to existing models. Our work
demonstrates that combining existing anonymization methods, such as regular
expressions, with machine learning can further reduce manual labor and enhance
automatic suggestions. | [
"Joel Niklaus",
"Robin Mamié",
"Matthias Stürmer",
"Daniel Brunner",
"Marcel Gygli"
] | 2023-10-07 00:56:49 | http://arxiv.org/abs/2310.04632v1 | http://arxiv.org/pdf/2310.04632v1 | 2310.04632v1 |
Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning | In many applications of federated learning (FL), clients desire models that
are personalized using their local data, yet are also robust in the sense that
they retain general global knowledge. However, the presence of data
heterogeneity across clients induces a fundamental trade-off between
personalization (i.e., adaptation to a local distribution) and robustness
(i.e., not forgetting previously learned general knowledge). It is critical to
understand how to navigate this personalization vs robustness trade-off when
designing federated systems, which are increasingly moving towards a paradigm
of fine-tuning large foundation models. Due to limited computational and
communication capabilities in most federated settings, this foundation model
fine-tuning must be done using parameter-efficient fine-tuning (PEFT)
approaches. While some recent work has studied federated approaches to PEFT,
the personalization vs robustness trade-off of federated PEFT has been largely
unexplored. In this work, we take a step towards bridging this gap by
benchmarking fundamental FL algorithms -- FedAvg and FedSGD plus
personalization (via client local fine-tuning) -- applied to one of the most
ubiquitous PEFT approaches to large language models (LLMs) -- prompt tuning --
in a multitude of hyperparameter settings under varying levels of data
heterogeneity. Our results show that federated-trained prompts can be
surprisingly robust when using a small learning rate with many local epochs for
personalization, especially when using an adaptive optimizer as the client
optimizer during federated training. We also demonstrate that simple approaches
such as adding regularization and interpolating two prompts are effective in
improving the personalization vs robustness trade-off in computation-limited
settings with few local updates allowed for personalization. | [
"Liam Collins",
"Shanshan Wu",
"Sewoong Oh",
"Khe Chai Sim"
] | 2023-10-06 23:46:33 | http://arxiv.org/abs/2310.04627v1 | http://arxiv.org/pdf/2310.04627v1 | 2310.04627v1 |
Copy Suppression: Comprehensively Understanding an Attention Head | We present a single attention head in GPT-2 Small that has one main role
across the entire training distribution. If components in earlier layers
predict a certain token, and this token appears earlier in the context, the
head suppresses it: we call this copy suppression. Attention Head 10.7 (L10H7)
suppresses naive copying behavior which improves overall model calibration.
This explains why multiple prior works studying certain narrow tasks found
negative heads that systematically favored the wrong answer. We uncover the
mechanism that the Negative Heads use for copy suppression with weights-based
evidence and are able to explain 76.9% of the impact of L10H7 in GPT-2 Small.
To the best of our knowledge, this is the most comprehensive description of the
complete role of a component in a language model to date. One major effect of
copy suppression is its role in self-repair. Self-repair refers to how ablating
crucial model components results in downstream neural network parts
compensating for this ablation. Copy suppression leads to self-repair: if an
initial overconfident copier is ablated, then there is nothing to suppress. We
show that self-repair is implemented by several mechanisms, one of which is
copy suppression, which explains 39% of the behavior in a narrow task.
Interactive visualisations of the copy suppression phenomena may be seen at our
web app https://copy-suppression.streamlit.app/ | [
"Callum McDougall",
"Arthur Conmy",
"Cody Rushing",
"Thomas McGrath",
"Neel Nanda"
] | 2023-10-06 23:37:24 | http://arxiv.org/abs/2310.04625v1 | http://arxiv.org/pdf/2310.04625v1 | 2310.04625v1 |
FluxGAN: A Physics-Aware Generative Adversarial Network Model for Generating Microstructures That Maintain Target Heat Flux | We propose a physics-aware generative adversarial network model, FluxGAN,
capable of simultaneously generating high-quality images of large
microstructures and description of their thermal properties. During the
training phase, the model learns about the relationship between the local
structural features and the physical processes, such as the heat flux in the
microstructures, due to external temperature gradients. Once trained, the model
generates new structural and associated heat flux environments, bypassing the
computationally expensive modeling. Our model provides a cost effective and
efficient approach over conventional modeling techniques, such as the finite
element method (FEM), for describing the thermal properties of microstructures.
The conventional approach requires computational modeling that scales with the
size of the microstructure model, therefore limiting the simulation to a given
size, resolution, and complexity of the model. In contrast, the FluxGAN model
uses synthesis-by-part approach and generates arbitrary large size images at
low computational cost. We demonstrate that the model can be utilized to
generate designs of thermal sprayed coatings that satisfies target thermal
properties. Furthermore, the model is capable of generating coating
microstructures and physical processes in three-dimensional (3D) domain after
being trained on two-dimensional (2D) examples. Our approach has the potential
to transform the design and optimization of thermal sprayed coatings for
various applications, including high-temperature and long-duration operation of
gas turbines for aircraft or ground-based power generators. | [
"Artem K. Pimachev",
"Manoj Settipalli",
"Sanghamitra Neogi"
] | 2023-10-06 23:13:40 | http://arxiv.org/abs/2310.04622v1 | http://arxiv.org/pdf/2310.04622v1 | 2310.04622v1 |
Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences | On-device machine learning (ML) promises to improve the privacy,
responsiveness, and proliferation of new, intelligent user experiences by
moving ML computation onto everyday personal devices. However, today's large ML
models must be drastically compressed to run efficiently on-device, a hurtle
that requires deep, yet currently niche expertise. To engage the broader
human-centered ML community in on-device ML experiences, we present the results
from an interview study with 30 experts at Apple that specialize in producing
efficient models. We compile tacit knowledge that experts have developed
through practical experience with model compression across different hardware
platforms. Our findings offer pragmatic considerations missing from prior work,
covering the design process, trade-offs, and technical strategies that go into
creating efficient models. Finally, we distill design recommendations for
tooling to help ease the difficulty of this work and bring on-device ML into to
more widespread practice. | [
"Fred Hohman",
"Mary Beth Kery",
"Donghao Ren",
"Dominik Moritz"
] | 2023-10-06 23:11:26 | http://arxiv.org/abs/2310.04621v1 | http://arxiv.org/pdf/2310.04621v1 | 2310.04621v1 |
SlotGNN: Unsupervised Discovery of Multi-Object Representations and Visual Dynamics | Learning multi-object dynamics from visual data using unsupervised techniques
is challenging due to the need for robust, object representations that can be
learned through robot interactions. This paper presents a novel framework with
two new architectures: SlotTransport for discovering object representations
from RGB images and SlotGNN for predicting their collective dynamics from RGB
images and robot interactions. Our SlotTransport architecture is based on slot
attention for unsupervised object discovery and uses a feature transport
mechanism to maintain temporal alignment in object-centric representations.
This enables the discovery of slots that consistently reflect the composition
of multi-object scenes. These slots robustly bind to distinct objects, even
under heavy occlusion or absence. Our SlotGNN, a novel unsupervised graph-based
dynamics model, predicts the future state of multi-object scenes. SlotGNN
learns a graph representation of the scene using the discovered slots from
SlotTransport and performs relational and spatial reasoning to predict the
future appearance of each slot conditioned on robot actions. We demonstrate the
effectiveness of SlotTransport in learning object-centric features that
accurately encode both visual and positional information. Further, we highlight
the accuracy of SlotGNN in downstream robotic tasks, including challenging
multi-object rearrangement and long-horizon prediction. Finally, our
unsupervised approach proves effective in the real world. With only minimal
additional data, our framework robustly predicts slots and their corresponding
dynamics in real-world control tasks. | [
"Alireza Rezazadeh",
"Athreyi Badithela",
"Karthik Desingh",
"Changhyun Choi"
] | 2023-10-06 22:37:34 | http://arxiv.org/abs/2310.04617v1 | http://arxiv.org/pdf/2310.04617v1 | 2310.04617v1 |
A Topological Perspective on Demystifying GNN-Based Link Prediction Performance | Graph Neural Networks (GNNs) have shown great promise in learning node
embeddings for link prediction (LP). While numerous studies aim to improve the
overall LP performance of GNNs, none have explored its varying performance
across different nodes and its underlying reasons. To this end, we aim to
demystify which nodes will perform better from the perspective of their local
topology. Despite the widespread belief that low-degree nodes exhibit poorer LP
performance, our empirical findings provide nuances to this viewpoint and
prompt us to propose a better metric, Topological Concentration (TC), based on
the intersection of the local subgraph of each node with the ones of its
neighbors. We empirically demonstrate that TC has a higher correlation with LP
performance than other node-level topological metrics like degree and subgraph
density, offering a better way to identify low-performing nodes than using
cold-start. With TC, we discover a novel topological distribution shift issue
in which newly joined neighbors of a node tend to become less interactive with
that node's existing neighbors, compromising the generalizability of node
embeddings for LP at testing time. To make the computation of TC scalable, We
further propose Approximated Topological Concentration (ATC) and
theoretically/empirically justify its efficacy in approximating TC and reducing
the computation complexity. Given the positive correlation between node TC and
its LP performance, we explore the potential of boosting LP performance via
enhancing TC by re-weighting edges in the message-passing and discuss its
effectiveness with limitations. Our code is publicly available at
https://github.com/YuWVandy/Topo_LP_GNN. | [
"Yu Wang",
"Tong Zhao",
"Yuying Zhao",
"Yunchao Liu",
"Xueqi Cheng",
"Neil Shah",
"Tyler Derr"
] | 2023-10-06 22:07:49 | http://arxiv.org/abs/2310.04612v1 | http://arxiv.org/pdf/2310.04612v1 | 2310.04612v1 |
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies | In the upcoming decade, deep learning may revolutionize the natural sciences,
enhancing our capacity to model and predict natural occurrences. This could
herald a new era of scientific exploration, bringing significant advancements
across sectors from drug development to renewable energy. To answer this call,
we present DeepSpeed4Science initiative (deepspeed4science.ai) which aims to
build unique capabilities through AI system technology innovations to help
domain experts to unlock today's biggest science mysteries. By leveraging
DeepSpeed's current technology pillars (training, inference and compression) as
base technology enablers, DeepSpeed4Science will create a new set of AI system
technologies tailored for accelerating scientific discoveries by addressing
their unique complexity beyond the common technical approaches used for
accelerating generic large language models (LLMs). In this paper, we showcase
the early progress we made with DeepSpeed4Science in addressing two of the
critical system challenges in structural biology research. | [
"Shuaiwen Leon Song",
"Bonnie Kruft",
"Minjia Zhang",
"Conglong Li",
"Shiyang Chen",
"Chengming Zhang",
"Masahiro Tanaka",
"Xiaoxia Wu",
"Jeff Rasley",
"Ammar Ahmad Awan",
"Connor Holmes",
"Martin Cai",
"Adam Ghanem",
"Zhongzhu Zhou",
"Yuxiong He",
"Pete Luferenko",
"Divya Kumar",
"Jonathan Weyn",
"Ruixiong Zhang",
"Sylwester Klocek",
"Volodymyr Vragov",
"Mohammed AlQuraishi",
"Gustaf Ahdritz",
"Christina Floristean",
"Cristina Negri",
"Rao Kotamarthi",
"Venkatram Vishwanath",
"Arvind Ramanathan",
"Sam Foreman",
"Kyle Hippe",
"Troy Arcomano",
"Romit Maulik",
"Maxim Zvyagin",
"Alexander Brace",
"Bin Zhang",
"Cindy Orozco Bohorquez",
"Austin Clyde",
"Bharat Kale",
"Danilo Perez-Rivera",
"Heng Ma",
"Carla M. Mann",
"Michael Irvin",
"J. Gregory Pauloski",
"Logan Ward",
"Valerie Hayot",
"Murali Emani",
"Zhen Xie",
"Diangen Lin",
"Maulik Shukla",
"Ian Foster",
"James J. Davis",
"Michael E. Papka",
"Thomas Brettin",
"Prasanna Balaprakash",
"Gina Tourassi",
"John Gounley",
"Heidi Hanson",
"Thomas E Potok",
"Massimiliano Lupo Pasini",
"Kate Evans",
"Dan Lu",
"Dalton Lunga",
"Junqi Yin",
"Sajal Dash",
"Feiyi Wang",
"Mallikarjun Shankar",
"Isaac Lyngaas",
"Xiao Wang",
"Guojing Cong",
"Pei Zhang",
"Ming Fan",
"Siyan Liu",
"Adolfy Hoisie",
"Shinjae Yoo",
"Yihui Ren",
"William Tang",
"Kyle Felker",
"Alexey Svyatkovskiy",
"Hang Liu",
"Ashwin Aji",
"Angela Dalton",
"Michael Schulte",
"Karl Schulz",
"Yuntian Deng",
"Weili Nie",
"Josh Romero",
"Christian Dallago",
"Arash Vahdat",
"Chaowei Xiao",
"Thomas Gibbs",
"Anima Anandkumar",
"Rick Stevens"
] | 2023-10-06 22:05:15 | http://arxiv.org/abs/2310.04610v2 | http://arxiv.org/pdf/2310.04610v2 | 2310.04610v2 |
A Comprehensive Performance Study of Large Language Models on Novel AI Accelerators | Artificial intelligence (AI) methods have become critical in scientific
applications to help accelerate scientific discovery. Large language models
(LLMs) are being considered as a promising approach to address some of the
challenging problems because of their superior generalization capabilities
across domains. The effectiveness of the models and the accuracy of the
applications is contingent upon their efficient execution on the underlying
hardware infrastructure. Specialized AI accelerator hardware systems have
recently become available for accelerating AI applications. However, the
comparative performance of these AI accelerators on large language models has
not been previously studied. In this paper, we systematically study LLMs on
multiple AI accelerators and GPUs and evaluate their performance
characteristics for these models. We evaluate these systems with (i) a
micro-benchmark using a core transformer block, (ii) a GPT- 2 model, and (iii)
an LLM-driven science use case, GenSLM. We present our findings and analyses of
the models' performance to better understand the intrinsic capabilities of AI
accelerators. Furthermore, our analysis takes into account key factors such as
sequence lengths, scaling behavior, sparsity, and sensitivity to gradient
accumulation steps. | [
"Murali Emani",
"Sam Foreman",
"Varuni Sastry",
"Zhen Xie",
"Siddhisanket Raskar",
"William Arnold",
"Rajeev Thakur",
"Venkatram Vishwanath",
"Michael E. Papka"
] | 2023-10-06 21:55:57 | http://arxiv.org/abs/2310.04607v1 | http://arxiv.org/pdf/2310.04607v1 | 2310.04607v1 |
Robust Transfer Learning with Unreliable Source Data | This paper addresses challenges in robust transfer learning stemming from
ambiguity in Bayes classifiers and weak transferable signals between the target
and source distribution. We introduce a novel quantity called the ''ambiguity
level'' that measures the discrepancy between the target and source regression
functions, propose a simple transfer learning procedure, and establish a
general theorem that shows how this new quantity is related to the
transferability of learning in terms of risk improvements. Our proposed
''Transfer Around Boundary'' (TAB) model, with a threshold balancing the
performance of target and source data, is shown to be both efficient and
robust, improving classification while avoiding negative transfer. Moreover, we
demonstrate the effectiveness of the TAB model on non-parametric classification
and logistic regression tasks, achieving upper bounds which are optimal up to
logarithmic factors. Simulation studies lend further support to the
effectiveness of TAB. We also provide simple approaches to bound the excess
misclassification error without the need for specialized knowledge in transfer
learning. | [
"Jianqing Fan",
"Cheng Gao",
"Jason M. Klusowski"
] | 2023-10-06 21:50:21 | http://arxiv.org/abs/2310.04606v1 | http://arxiv.org/pdf/2310.04606v1 | 2310.04606v1 |
Learning Optimal Power Flow Value Functions with Input-Convex Neural Networks | The Optimal Power Flow (OPF) problem is integral to the functioning of power
systems, aiming to optimize generation dispatch while adhering to technical and
operational constraints. These constraints are far from straightforward; they
involve intricate, non-convex considerations related to Alternating Current
(AC) power flow, which are essential for the safety and practicality of
electrical grids. However, solving the OPF problem for varying conditions
within stringent time frames poses practical challenges. To address this,
operators resort to model simplifications of varying accuracy. Unfortunately,
better approximations (tight convex relaxations) are often computationally
intractable. This research explores machine learning (ML) to learn convex
approximate solutions for faster analysis in the online setting while still
allowing for coupling into other convex dependent decision problems. By trading
off a small amount of accuracy for substantial gains in speed, they enable the
efficient exploration of vast solution spaces in these complex problems. | [
"Andrew Rosemberg",
"Mathieu Tanneau",
"Bruno Fanzeres",
"Joaquim Garcia",
"Pascal Van Hentenryck"
] | 2023-10-06 21:48:39 | http://arxiv.org/abs/2310.04605v1 | http://arxiv.org/pdf/2310.04605v1 | 2310.04605v1 |
PriViT: Vision Transformers for Fast Private Inference | The Vision Transformer (ViT) architecture has emerged as the backbone of
choice for state-of-the-art deep models for computer vision applications.
However, ViTs are ill-suited for private inference using secure multi-party
computation (MPC) protocols, due to the large number of non-polynomial
operations (self-attention, feed-forward rectifiers, layer normalization). We
propose PriViT, a gradient based algorithm to selectively "Taylorize"
nonlinearities in ViTs while maintaining their prediction accuracy. Our
algorithm is conceptually simple, easy to implement, and achieves improved
performance over existing approaches for designing MPC-friendly transformer
architectures in terms of achieving the Pareto frontier in latency-accuracy. We
confirm these improvements via experiments on several standard image
classification tasks. Public code is available at
https://github.com/NYU-DICE-Lab/privit. | [
"Naren Dhyani",
"Jianqiao Mo",
"Minsu Cho",
"Ameya Joshi",
"Siddharth Garg",
"Brandon Reagen",
"Chinmay Hegde"
] | 2023-10-06 21:45:05 | http://arxiv.org/abs/2310.04604v1 | http://arxiv.org/pdf/2310.04604v1 | 2310.04604v1 |
A neuro-symbolic framework for answering conjunctive queries | The problem of answering logical queries over incomplete knowledge graphs is
receiving significant attention in the machine learning community.
Neuro-symbolic models are a promising recent approach, showing good performance
and allowing for good interpretability properties. These models rely on trained
architectures to execute atomic queries, combining them with modules that
simulate the symbolic operators in queries. Unfortunately, most neuro-symbolic
query processors are limited to the so-called tree-like logical queries that
admit a bottom-up execution, where the leaves are constant values or anchors,
and the root is the target variable. Tree-like queries, while expressive, fail
short to express properties in knowledge graphs that are important in practice,
such as the existence of multiple edges between entities or the presence of
triangles.
We propose a framework for answering arbitrary conjunctive queries over
incomplete knowledge graphs. The main idea of our method is to approximate a
cyclic query by an infinite family of tree-like queries, and then leverage
existing models for the latter. Our approximations achieve strong guarantees:
they are complete, i.e. there are no false negatives, and optimal, i.e. they
provide the best possible approximation using tree-like queries. Our method
requires the approximations to be tree-like queries where the leaves are
anchors or existentially quantified variables. Hence, we also show how some of
the existing neuro-symbolic models can handle these queries, which is of
independent interest. Experiments show that our approximation strategy achieves
competitive results, and that including queries with existentially quantified
variables tends to improve the general performance of these models, both on
tree-like queries and on our approximation strategy. | [
"Pablo Barceló",
"Tamara Cucumides",
"Floris Geerts",
"Juan Reutter",
"Miguel Romero"
] | 2023-10-06 21:31:17 | http://arxiv.org/abs/2310.04598v1 | http://arxiv.org/pdf/2310.04598v1 | 2310.04598v1 |
Deep Model Predictive Optimization | A major challenge in robotics is to design robust policies which enable
complex and agile behaviors in the real world. On one end of the spectrum, we
have model-free reinforcement learning (MFRL), which is incredibly flexible and
general but often results in brittle policies. In contrast, model predictive
control (MPC) continually re-plans at each time step to remain robust to
perturbations and model inaccuracies. However, despite its real-world
successes, MPC often under-performs the optimal strategy. This is due to model
quality, myopic behavior from short planning horizons, and approximations due
to computational constraints. And even with a perfect model and enough compute,
MPC can get stuck in bad local optima, depending heavily on the quality of the
optimization algorithm. To this end, we propose Deep Model Predictive
Optimization (DMPO), which learns the inner-loop of an MPC optimization
algorithm directly via experience, specifically tailored to the needs of the
control problem. We evaluate DMPO on a real quadrotor agile trajectory tracking
task, on which it improves performance over a baseline MPC algorithm for a
given computational budget. It can outperform the best MPC algorithm by up to
27% with fewer samples and an end-to-end policy trained with MFRL by 19%.
Moreover, because DMPO requires fewer samples, it can also achieve these
benefits with 4.3X less memory. When we subject the quadrotor to turbulent wind
fields with an attached drag plate, DMPO can adapt zero-shot while still
outperforming all baselines. Additional results can be found at
https://tinyurl.com/mr2ywmnw. | [
"Jacob Sacks",
"Rwik Rana",
"Kevin Huang",
"Alex Spitzer",
"Guanya Shi",
"Byron Boots"
] | 2023-10-06 21:11:52 | http://arxiv.org/abs/2310.04590v1 | http://arxiv.org/pdf/2310.04590v1 | 2310.04590v1 |
The Impact of Equal Opportunity on Statistical Discrimination | I modify the canonical statistical discrimination model of Coate and Loury
(1993) by assuming the firm's belief about an individual's unobserved class is
machine learning-generated and, therefore, contractible. This expands the
toolkit of a regulator beyond belief-free regulations like affirmative action.
Contractible beliefs make it feasible to require the firm to select a decision
policy that equalizes true positive rates across groups -- what the algorithmic
fairness literature calls equal opportunity. While affirmative action does not
necessarily end statistical discrimination, I show that imposing equal
opportunity does. | [
"John Y. Zhu"
] | 2023-10-06 20:57:34 | http://arxiv.org/abs/2310.04585v1 | http://arxiv.org/pdf/2310.04585v1 | 2310.04585v1 |
Self-Confirming Transformer for Locally Consistent Online Adaptation in Multi-Agent Reinforcement Learning | Offline reinforcement learning (RL) leverages previously collected data to
extract policies that return satisfying performance in online environments.
However, offline RL suffers from the distribution shift between the offline
dataset and the online environment. In the multi-agent RL (MARL) setting, this
distribution shift may arise from the nonstationary opponents (exogenous agents
beyond control) in the online testing who display distinct behaviors from those
recorded in the offline dataset. Hence, the key to the broader deployment of
offline MARL is the online adaptation to nonstationary opponents. Recent
advances in large language models have demonstrated the surprising
generalization ability of the transformer architecture in sequence modeling,
which prompts one to wonder \textit{whether the offline-trained transformer
policy adapts to nonstationary opponents during online testing}. This work
proposes the self-confirming loss (SCL) in offline transformer training to
address the online nonstationarity, which is motivated by the self-confirming
equilibrium (SCE) in game theory. The gist is that the transformer learns to
predict the opponents' future moves based on which it acts accordingly. As a
weaker variant of Nash equilibrium (NE), SCE (equivalently, SCL) only requires
local consistency: the agent's local observations do not deviate from its
conjectures, leading to a more adaptable policy than the one dictated by NE
focusing on global optimality. We evaluate the online adaptability of the
self-confirming transformer (SCT) by playing against nonstationary opponents
employing a variety of policies, from the random one to the benchmark MARL
policies. Experimental results demonstrate that SCT can adapt to nonstationary
opponents online, achieving higher returns than vanilla transformers and
offline MARL baselines. | [
"Tao Li",
"Juan Guevara",
"Xinghong Xie",
"Quanyan Zhu"
] | 2023-10-06 20:43:08 | http://arxiv.org/abs/2310.04579v1 | http://arxiv.org/pdf/2310.04579v1 | 2310.04579v1 |
Can pruning make Large Language Models more efficient? | Transformer models have revolutionized natural language processing with their
unparalleled ability to grasp complex contextual relationships. However, the
vast number of parameters in these models has raised concerns regarding
computational efficiency, environmental impact, and deployability on
resource-limited platforms. To address these challenges, this paper
investigates the application of weight pruning-a strategic reduction of model
parameters based on their significance-as an optimization strategy for
Transformer architectures. Through extensive experimentation, we explore
various pruning methodologies, highlighting their impact on model performance,
size, and computational demands. Our findings suggest that with judicious
selection of pruning hyperparameters, significant reductions in model size are
attainable without considerable compromise on performance. Moreover, when
coupled with post-pruning fine-tuning strategies, some pruned models even
exhibit enhanced generalization capabilities. This work seeks to bridge the gap
between model efficiency and performance, paving the way for more scalable and
environmentally responsible deep learning applications. | [
"Sia Gholami",
"Marwan Omar"
] | 2023-10-06 20:28:32 | http://arxiv.org/abs/2310.04573v1 | http://arxiv.org/pdf/2310.04573v1 | 2310.04573v1 |
Transformer-Based Neural Surrogate for Link-Level Path Loss Prediction from Variable-Sized Maps | Estimating path loss for a transmitter-receiver location is key to many
use-cases including network planning and handover. Machine learning has become
a popular tool to predict wireless channel properties based on map data. In
this work, we present a transformer-based neural network architecture that
enables predicting link-level properties from maps of various dimensions and
from sparse measurements. The map contains information about buildings and
foliage. The transformer model attends to the regions that are relevant for
path loss prediction and, therefore, scales efficiently to maps of different
size. Further, our approach works with continuous transmitter and receiver
coordinates without relying on discretization. In experiments, we show that the
proposed model is able to efficiently learn dominant path losses from sparse
training data and generalizes well when tested on novel maps. | [
"Thomas M. Hehn",
"Tribhuvanesh Orekondy",
"Ori Shental",
"Arash Behboodi",
"Juan Bucheli",
"Akash Doshi",
"June Namgoong",
"Taesang Yoo",
"Ashwin Sampath",
"Joseph B. Soriaga"
] | 2023-10-06 20:17:40 | http://arxiv.org/abs/2310.04570v2 | http://arxiv.org/pdf/2310.04570v2 | 2310.04570v2 |
Knolling bot: A Transformer-based Approach to Organizing a Messy Table | In this study, we propose an approach to equip domestic robots with the
ability to perform simple household tidying tasks. We focus specifically on
'knolling,' an activity related to organizing scattered items into neat and
space-efficient arrangements. Unlike the uniformity of industrial environments,
household settings present unique challenges due to their diverse array of
items and the subjectivity of tidiness. Here, we draw inspiration from natural
language processing (NLP) and utilize a transformer-based approach that
predicts the next position of an item in a sequence of neatly positioned items.
We integrate the knolling model with a visual perception model and a physical
robot arm to demonstrate a machine that declutters and organizes a dozen
freeform items of various shapes and sizes. | [
"Yuhang Hu",
"Zhizhuo Zhang",
"Ruibo Liu",
"Philippe Wyder",
"Hod Lipson"
] | 2023-10-06 20:13:07 | http://arxiv.org/abs/2310.04566v1 | http://arxiv.org/pdf/2310.04566v1 | 2310.04566v1 |
Binary Quantification and Dataset Shift: An Experimental Investigation | Quantification is the supervised learning task that consists of training
predictors of the class prevalence values of sets of unlabelled data, and is of
special interest when the labelled data on which the predictor has been trained
and the unlabelled data are not IID, i.e., suffer from dataset shift. To date,
quantification methods have mostly been tested only on a special case of
dataset shift, i.e., prior probability shift; the relationship between
quantification and other types of dataset shift remains, by and large,
unexplored. In this work we carry out an experimental analysis of how current
quantification algorithms behave under different types of dataset shift, in
order to identify limitations of current approaches and hopefully pave the way
for the development of more broadly applicable methods. We do this by proposing
a fine-grained taxonomy of types of dataset shift, by establishing protocols
for the generation of datasets affected by these types of shift, and by testing
existing quantification methods on the datasets thus generated. One finding
that results from this investigation is that many existing quantification
methods that had been found robust to prior probability shift are not
necessarily robust to other types of dataset shift. A second finding is that no
existing quantification method seems to be robust enough to dealing with all
the types of dataset shift we simulate in our experiments. The code needed to
reproduce all our experiments is publicly available at
https://github.com/pglez82/quant_datasetshift. | [
"Pablo González",
"Alejandro Moreo",
"Fabrizio Sebastiani"
] | 2023-10-06 20:11:27 | http://arxiv.org/abs/2310.04565v1 | http://arxiv.org/pdf/2310.04565v1 | 2310.04565v1 |
ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models | Large Language Models (LLMs) with billions of parameters have drastically
transformed AI applications. However, their demanding computation during
inference has raised significant challenges for deployment on
resource-constrained devices. Despite recent trends favoring alternative
activation functions such as GELU or SiLU, known for increased computation,
this study strongly advocates for reinstating ReLU activation in LLMs. We
demonstrate that using the ReLU activation function has a negligible impact on
convergence and performance while significantly reducing computation and weight
transfer. This reduction is particularly valuable during the memory-bound
inference step, where efficiency is paramount. Exploring sparsity patterns in
ReLU-based LLMs, we unveil the reutilization of activated neurons for
generating new tokens and leveraging these insights, we propose practical
strategies to substantially reduce LLM inference computation up to three times,
using ReLU activations with minimal performance trade-offs. | [
"Iman Mirzadeh",
"Keivan Alizadeh",
"Sachin Mehta",
"Carlo C Del Mundo",
"Oncel Tuzel",
"Golnoosh Samei",
"Mohammad Rastegari",
"Mehrdad Farajtabar"
] | 2023-10-06 20:01:33 | http://arxiv.org/abs/2310.04564v1 | http://arxiv.org/pdf/2310.04564v1 | 2310.04564v1 |
DragD3D: Vertex-based Editing for Realistic Mesh Deformations using 2D Diffusion Priors | Direct mesh editing and deformation are key components in the geometric
modeling and animation pipeline. Direct mesh editing methods are typically
framed as optimization problems combining user-specified vertex constraints
with a regularizer that determines the position of the rest of the vertices.
The choice of the regularizer is key to the realism and authenticity of the
final result. Physics and geometry-based regularizers are not aware of the
global context and semantics of the object, and the more recent deep learning
priors are limited to a specific class of 3D object deformations. In this work,
our main contribution is a local mesh editing method called DragD3D for global
context-aware realistic deformation through direct manipulation of a few
vertices. DragD3D is not restricted to any class of objects. It achieves this
by combining the classic geometric ARAP (as rigid as possible) regularizer with
2D priors obtained from a large-scale diffusion model. Specifically, we render
the objects from multiple viewpoints through a differentiable renderer and use
the recently introduced DDS loss which scores the faithfulness of the rendered
image to one from a diffusion model. DragD3D combines the approximate gradients
of the DDS with gradients from the ARAP loss to modify the mesh vertices via
neural Jacobian field, while also satisfying vertex constraints. We show that
our deformations are realistic and aware of the global context of the objects,
and provide better results than just using geometric regularizers. | [
"Tianhao Xie",
"Eugene Belilovsky",
"Sudhir Mudur",
"Tiberiu Popa"
] | 2023-10-06 19:55:40 | http://arxiv.org/abs/2310.04561v1 | http://arxiv.org/pdf/2310.04561v1 | 2310.04561v1 |
Talk like a Graph: Encoding Graphs for Large Language Models | Graphs are a powerful tool for representing and analyzing complex
relationships in real-world applications such as social networks, recommender
systems, and computational finance. Reasoning on graphs is essential for
drawing inferences about the relationships between entities in a complex
system, and to identify hidden patterns and trends. Despite the remarkable
progress in automated reasoning with natural text, reasoning on graphs with
large language models (LLMs) remains an understudied problem. In this work, we
perform the first comprehensive study of encoding graph-structured data as text
for consumption by LLMs. We show that LLM performance on graph reasoning tasks
varies on three fundamental levels: (1) the graph encoding method, (2) the
nature of the graph task itself, and (3) interestingly, the very structure of
the graph considered. These novel results provide valuable insight on
strategies for encoding graphs as text. Using these insights we illustrate how
the correct choice of encoders can boost performance on graph reasoning tasks
inside LLMs by 4.8% to 61.8%, depending on the task. | [
"Bahare Fatemi",
"Jonathan Halcrow",
"Bryan Perozzi"
] | 2023-10-06 19:55:21 | http://arxiv.org/abs/2310.04560v1 | http://arxiv.org/pdf/2310.04560v1 | 2310.04560v1 |
VTON-IT: Virtual Try-On using Image Translation | Virtual Try-On (trying clothes virtually) is a promising application of the
Generative Adversarial Network (GAN). However, it is an arduous task to
transfer the desired clothing item onto the corresponding regions of a human
body because of varying body size, pose, and occlusions like hair and
overlapped clothes. In this paper, we try to produce photo-realistic translated
images through semantic segmentation and a generative adversarial
architecture-based image translation network. We present a novel image-based
Virtual Try-On application VTON-IT that takes an RGB image, segments desired
body part, and overlays target cloth over the segmented body region. Most
state-of-the-art GAN-based Virtual Try-On applications produce unaligned
pixelated synthesis images on real-life test images. However, our approach
generates high-resolution natural images with detailed textures on such variant
images. | [
"Santosh Adhikari",
"Bishnu Bhusal",
"Prashant Ghimire",
"Anil Shrestha"
] | 2023-10-06 19:47:20 | http://arxiv.org/abs/2310.04558v1 | http://arxiv.org/pdf/2310.04558v1 | 2310.04558v1 |
Module-wise Adaptive Distillation for Multimodality Foundation Models | Pre-trained multimodal foundation models have demonstrated remarkable
generalizability but pose challenges for deployment due to their large sizes.
One effective approach to reducing their sizes is layerwise distillation,
wherein small student models are trained to match the hidden representations of
large teacher models at each layer. Motivated by our observation that certain
architecture components, referred to as modules, contribute more significantly
to the student's performance than others, we propose to track the contributions
of individual modules by recording the loss decrement after distillation each
module and choose the module with a greater contribution to distill more
frequently. Such an approach can be naturally formulated as a multi-armed
bandit (MAB) problem, where modules and loss decrements are considered as arms
and rewards, respectively. We then develop a modified-Thompson sampling
algorithm named OPTIMA to address the nonstationarity of module contributions
resulting from model updating. Specifically, we leverage the observed
contributions in recent history to estimate the changing contribution of each
module and select modules based on these estimations to maximize the cumulative
contribution. We evaluate the effectiveness of OPTIMA through distillation
experiments on various multimodal understanding and image captioning tasks,
using the CoCa-Large model (Yu et al., 2022) as the teacher model. | [
"Chen Liang",
"Jiahui Yu",
"Ming-Hsuan Yang",
"Matthew Brown",
"Yin Cui",
"Tuo Zhao",
"Boqing Gong",
"Tianyi Zhou"
] | 2023-10-06 19:24:00 | http://arxiv.org/abs/2310.04550v1 | http://arxiv.org/pdf/2310.04550v1 | 2310.04550v1 |
Multi-decadal Sea Level Prediction using Neural Networks and Spectral Clustering on Climate Model Large Ensembles and Satellite Altimeter Data | Sea surface height observations provided by satellite altimetry since 1993
show a rising rate (3.4 mm/year) for global mean sea level. While on average,
sea level has risen 10 cm over the last 30 years, there is considerable
regional variation in the sea level change. Through this work, we predict sea
level trends 30 years into the future at a 2-degree spatial resolution and
investigate the future patterns of the sea level change. We show the potential
of machine learning (ML) in this challenging application of long-term sea level
forecasting over the global ocean. Our approach incorporates sea level data
from both altimeter observations and climate model simulations. We develop a
supervised learning framework using fully connected neural networks (FCNNs)
that can predict the sea level trend based on climate model projections.
Alongside this, our method provides uncertainty estimates associated with the
ML prediction. We also show the effectiveness of partitioning our spatial
dataset and learning a dedicated ML model for each segmented region. We compare
two partitioning strategies: one achieved using domain knowledge, and the other
employing spectral clustering. Our results demonstrate that segmenting the
spatial dataset with spectral clustering improves the ML predictions. | [
"Saumya Sinha",
"John Fasullo",
"R. Steven Nerem",
"Claire Monteleoni"
] | 2023-10-06 19:06:43 | http://arxiv.org/abs/2310.04540v1 | http://arxiv.org/pdf/2310.04540v1 | 2310.04540v1 |
Generating Less Certain Adversarial Examples Improves Robust Generalization | Recent studies have shown that deep neural networks are vulnerable to
adversarial examples. Numerous defenses have been proposed to improve model
robustness, among which adversarial training is most successful. In this work,
we revisit the robust overfitting phenomenon. In particular, we argue that
overconfident models produced during adversarial training could be a potential
cause, supported by the empirical observation that the predicted labels of
adversarial examples generated by models with better robust generalization
ability tend to have significantly more even distributions. Based on the
proposed definition of adversarial certainty, we incorporate an extragradient
step in the adversarial training framework to search for models that can
generate adversarially perturbed inputs with lower certainty, further improving
robust generalization. Our approach is general and can be easily combined with
other variants of adversarial training methods. Extensive experiments on image
benchmarks demonstrate that our method effectively alleviates robust
overfitting and is able to produce models with consistently improved
robustness. | [
"Minxing Zhang",
"Michael Backes",
"Xiao Zhang"
] | 2023-10-06 19:06:13 | http://arxiv.org/abs/2310.04539v1 | http://arxiv.org/pdf/2310.04539v1 | 2310.04539v1 |
LLM4DV: Using Large Language Models for Hardware Test Stimuli Generation | Test stimuli generation has been a crucial but labor-intensive task in
hardware design verification. In this paper, we revolutionize this process by
harnessing the power of large language models (LLMs) and present a novel
benchmarking framework, LLM4DV. This framework introduces a prompt template for
interactively eliciting test stimuli from the LLM, along with four innovative
prompting improvements to support the pipeline execution and further enhance
its performance. We compare LLM4DV to traditional constrained-random testing
(CRT), using three self-designed design-under-test (DUT) modules. Experiments
demonstrate that LLM4DV excels in efficiently handling straightforward DUT
scenarios, leveraging its ability to employ basic mathematical reasoning and
pre-trained knowledge. While it exhibits reduced efficiency in complex task
settings, it still outperforms CRT in relative terms. The proposed framework
and the DUT modules used in our experiments will be open-sourced upon
publication. | [
"Zixi Zhang",
"Greg Chadwick",
"Hugo McNally",
"Yiren Zhao",
"Robert Mullins"
] | 2023-10-06 19:02:04 | http://arxiv.org/abs/2310.04535v1 | http://arxiv.org/pdf/2310.04535v1 | 2310.04535v1 |
DPGOMI: Differentially Private Data Publishing with Gaussian Optimized Model Inversion | High-dimensional data are widely used in the era of deep learning with
numerous applications. However, certain data which has sensitive information
are not allowed to be shared without privacy protection. In this paper, we
propose a novel differentially private data releasing method called
Differentially Private Data Publishing with Gaussian Optimized Model Inversion
(DPGOMI) to address this issue. Our approach involves mapping private data to
the latent space using a public generator, followed by a lower-dimensional
DP-GAN with better convergence properties. We evaluate the performance of
DPGOMI on standard datasets CIFAR10 and SVHN. Our results show that DPGOMI
outperforms the standard DP-GAN method in terms of Inception Score, Fr\'echet
Inception Distance, and classification performance, while providing the same
level of privacy. Our proposed approach offers a promising solution for
protecting sensitive data in GAN training while maintaining high-quality
results. | [
"Dongjie Chen",
"Sen-ching S. Cheung",
"Chen-Nee Chuah"
] | 2023-10-06 18:46:22 | http://arxiv.org/abs/2310.04528v1 | http://arxiv.org/pdf/2310.04528v1 | 2310.04528v1 |
Lie Neurons: Adjoint-Equivariant Neural Networks for Semisimple Lie Algebras | This paper proposes an adjoint-equivariant neural network that takes Lie
algebra data as input. Various types of equivariant neural networks have been
proposed in the literature, which treat the input data as elements in a vector
space carrying certain types of transformations. In comparison, we aim to
process inputs that are transformations between vector spaces. The change of
basis on transformation is described by conjugations, inducing the
adjoint-equivariance relationship that our model is designed to capture.
Leveraging the invariance property of the Killing form, the proposed network is
a general framework that works for arbitrary semisimple Lie algebras. Our
network possesses a simple structure that can be viewed as a Lie algebraic
generalization of a multi-layer perceptron (MLP). This work extends the
application of equivariant feature learning. As an example, we showcase its
value in homography modeling using sl(3) Lie algebra. | [
"Tzu-Yuan Lin",
"Minghan Zhu",
"Maani Ghaffari"
] | 2023-10-06 18:34:27 | http://arxiv.org/abs/2310.04521v1 | http://arxiv.org/pdf/2310.04521v1 | 2310.04521v1 |
SPADE: Sparsity-Guided Debugging for Deep Neural Networks | Interpretability, broadly defined as mechanisms for understanding why and how
machine learning models reach their decisions, is one of the key open goals at
the intersection of deep learning theory and practice. Towards this goal,
multiple tools have been proposed to aid a human examiner in reasoning about a
network's behavior in general or on a set of instances. However, the outputs of
these tools-such as input saliency maps or neuron visualizations-are frequently
difficult for a human to interpret, or even misleading, due, in particular, to
the fact that neurons can be multifaceted, i.e., a single neuron can be
associated with multiple distinct feature combinations. In this paper, we
present a new general approach to address this problem, called SPADE, which,
given a trained model and a target sample, uses sample-targeted pruning to
provide a "trace" of the network's execution on the sample, reducing the
network to the connections that are most relevant to the specific prediction.
We demonstrate that preprocessing with SPADE significantly increases both the
accuracy of image saliency maps across several interpretability methods and the
usefulness of neuron visualizations, aiding humans in reasoning about network
behavior. Our findings show that sample-specific pruning of connections can
disentangle multifaceted neurons, leading to consistently improved
interpretability. | [
"Arshia Soltani Moakhar",
"Eugenia Iofinova",
"Dan Alistarh"
] | 2023-10-06 18:28:33 | http://arxiv.org/abs/2310.04519v1 | http://arxiv.org/pdf/2310.04519v1 | 2310.04519v1 |
Domain Randomization for Sim2real Transfer of Automatically Generated Grasping Datasets | Robotic grasping refers to making a robotic system pick an object by applying
forces and torques on its surface. Many recent studies use data-driven
approaches to address grasping, but the sparse reward nature of this task made
the learning process challenging to bootstrap. To avoid constraining the
operational space, an increasing number of works propose grasping datasets to
learn from. But most of them are limited to simulations. The present paper
investigates how automatically generated grasps can be exploited in the real
world. More than 7000 reach-and-grasp trajectories have been generated with
Quality-Diversity (QD) methods on 3 different arms and grippers, including
parallel fingers and a dexterous hand, and tested in the real world. Conducted
analysis on the collected measure shows correlations between several Domain
Randomization-based quality criteria and sim-to-real transferability. Key
challenges regarding the reality gap for grasping have been identified,
stressing matters on which researchers on grasping should focus in the future.
A QD approach has finally been proposed for making grasps more robust to domain
randomization, resulting in a transfer ratio of 84% on the Franka Research 3
arm. | [
"Johann Huber",
"François Hélénon",
"Hippolyte Watrelot",
"Faiz Ben Amar",
"Stéphane Doncieux"
] | 2023-10-06 18:26:09 | http://arxiv.org/abs/2310.04517v1 | http://arxiv.org/pdf/2310.04517v1 | 2310.04517v1 |
Utilizing Free Clients in Federated Learning for Focused Model Enhancement | Federated Learning (FL) is a distributed machine learning approach to learn
models on decentralized heterogeneous data, without the need for clients to
share their data. Many existing FL approaches assume that all clients have
equal importance and construct a global objective based on all clients. We
consider a version of FL we call Prioritized FL, where the goal is to learn a
weighted mean objective of a subset of clients, designated as priority clients.
An important question arises: How do we choose and incentivize well aligned non
priority clients to participate in the federation, while discarding misaligned
clients? We present FedALIGN (Federated Adaptive Learning with Inclusion of
Global Needs) to address this challenge. The algorithm employs a matching
strategy that chooses non priority clients based on how similar the models loss
is on their data compared to the global data, thereby ensuring the use of non
priority client gradients only when it is beneficial for priority clients. This
approach ensures mutual benefits as non priority clients are motivated to join
when the model performs satisfactorily on their data, and priority clients can
utilize their updates and computational resources when their goals align. We
present a convergence analysis that quantifies the trade off between client
selection and speed of convergence. Our algorithm shows faster convergence and
higher test accuracy than baselines for various synthetic and benchmark
datasets. | [
"Aditya Narayan Ravi",
"Ilan Shomorony"
] | 2023-10-06 18:23:40 | http://arxiv.org/abs/2310.04515v1 | http://arxiv.org/pdf/2310.04515v1 | 2310.04515v1 |
URLOST: Unsupervised Representation Learning without Stationarity or Topology | Unsupervised representation learning has seen tremendous progress but is
constrained by its reliance on data modality-specific stationarity and
topology, a limitation not found in biological intelligence systems. For
instance, human vision processes visual signals derived from irregular and
non-stationary sampling lattices yet accurately perceives the geometry of the
world. We introduce a novel framework that learns from high-dimensional data
lacking stationarity and topology. Our model combines a learnable
self-organizing layer, density adjusted spectral clustering, and masked
autoencoders. We evaluate its effectiveness on simulated biological vision
data, neural recordings from the primary visual cortex, and gene expression
datasets. Compared to state-of-the-art unsupervised learning methods like
SimCLR and MAE, our model excels at learning meaningful representations across
diverse modalities without depending on stationarity or topology. It also
outperforms other methods not dependent on these factors, setting a new
benchmark in the field. This work represents a step toward unsupervised
learning methods that can generalize across diverse high-dimensional data
modalities. | [
"Zeyu Yun",
"Juexiao Zhang",
"Bruno Olshausen",
"Yann LeCun",
"Yubei Chen"
] | 2023-10-06 18:00:02 | http://arxiv.org/abs/2310.04496v1 | http://arxiv.org/pdf/2310.04496v1 | 2310.04496v1 |
Generative Diffusion From An Action Principle | Generative diffusion models synthesize new samples by reversing a diffusive
process that converts a given data set to generic noise. This is accomplished
by training a neural network to match the gradient of the log of the
probability distribution of a given data set, also called the score. By casting
reverse diffusion as an optimal control problem, we show that score matching
can be derived from an action principle, like the ones commonly used in
physics. We use this insight to demonstrate the connection between different
classes of diffusion models. | [
"Akhil Premkumar"
] | 2023-10-06 18:00:00 | http://arxiv.org/abs/2310.04490v1 | http://arxiv.org/pdf/2310.04490v1 | 2310.04490v1 |
BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity | Understanding the functional organization of higher visual cortex is a
central focus in neuroscience. Past studies have primarily mapped the visual
and semantic selectivity of neural populations using hand-selected stimuli,
which may potentially bias results towards pre-existing hypotheses of visual
cortex functionality. Moving beyond conventional approaches, we introduce a
data-driven method that generates natural language descriptions for images
predicted to maximally activate individual voxels of interest. Our method --
Semantic Captioning Using Brain Alignments ("BrainSCUBA") -- builds upon the
rich embedding space learned by a contrastive vision-language model and
utilizes a pre-trained large language model to generate interpretable captions.
We validate our method through fine-grained voxel-level captioning across
higher-order visual regions. We further perform text-conditioned image
synthesis with the captions, and show that our images are semantically coherent
and yield high predicted activations. Finally, to demonstrate how our method
enables scientific discovery, we perform exploratory investigations on the
distribution of "person" representations in the brain, and discover
fine-grained semantic selectivity in body-selective areas. Unlike earlier
studies that decode text, our method derives voxel-wise captions of semantic
selectivity. Our results show that BrainSCUBA is a promising means for
understanding functional preferences in the brain, and provides motivation for
further hypothesis-driven investigation of visual cortex. | [
"Andrew F. Luo",
"Margaret M. Henderson",
"Michael J. Tarr",
"Leila Wehbe"
] | 2023-10-06 17:59:53 | http://arxiv.org/abs/2310.04420v1 | http://arxiv.org/pdf/2310.04420v1 | 2310.04420v1 |
Functional Interpolation for Relative Positions Improves Long Context Transformers | Preventing the performance decay of Transformers on inputs longer than those
used for training has been an important challenge in extending the context
length of these models. Though the Transformer architecture has fundamentally
no limits on the input sequence lengths it can process, the choice of position
encoding used during training can limit the performance of these models on
longer inputs. We propose a novel functional relative position encoding with
progressive interpolation, FIRE, to improve Transformer generalization to
longer contexts. We theoretically prove that this can represent some of the
popular relative position encodings, such as T5's RPE, Alibi, and Kerple. We
next empirically show that FIRE models have better generalization to longer
contexts on both zero-shot language modeling and long text benchmarks. | [
"Shanda Li",
"Chong You",
"Guru Guruganesh",
"Joshua Ainslie",
"Santiago Ontanon",
"Manzil Zaheer",
"Sumit Sanghai",
"Yiming Yang",
"Sanjiv Kumar",
"Srinadh Bhojanapalli"
] | 2023-10-06 17:59:11 | http://arxiv.org/abs/2310.04418v1 | http://arxiv.org/pdf/2310.04418v1 | 2310.04418v1 |
Diffusion Random Feature Model | Diffusion probabilistic models have been successfully used to generate data
from noise. However, most diffusion models are computationally expensive and
difficult to interpret with a lack of theoretical justification. Random feature
models on the other hand have gained popularity due to their interpretability
but their application to complex machine learning tasks remains limited. In
this work, we present a diffusion model-inspired deep random feature model that
is interpretable and gives comparable numerical results to a fully connected
neural network having the same number of trainable parameters. Specifically, we
extend existing results for random features and derive generalization bounds
between the distribution of sampled data and the true distribution using
properties of score matching. We validate our findings by generating samples on
the fashion MNIST dataset and instrumental audio data. | [
"Esha Saha",
"Giang Tran"
] | 2023-10-06 17:59:05 | http://arxiv.org/abs/2310.04417v2 | http://arxiv.org/pdf/2310.04417v2 | 2310.04417v2 |
Why Do We Need Weight Decay in Modern Deep Learning? | Weight decay is a broadly used technique for training state-of-the-art deep
networks, including large language models. Despite its widespread usage, its
role remains poorly understood. In this work, we highlight that the role of
weight decay in modern deep learning is different from its regularization
effect studied in classical learning theory. For overparameterized deep
networks, we show how weight decay modifies the optimization dynamics enhancing
the ever-present implicit regularization of SGD via the loss stabilization
mechanism. In contrast, for underparameterized large language models trained
with nearly online SGD, we describe how weight decay balances the bias-variance
tradeoff in stochastic optimization leading to lower training loss. Moreover,
we show that weight decay also prevents sudden loss divergences for bfloat16
mixed-precision training which is a crucial tool for LLM training. Overall, we
present a unifying perspective from ResNets on vision tasks to LLMs: weight
decay is never useful as an explicit regularizer but instead changes the
training dynamics in a desirable way. Our code is available at
https://github.com/tml-epfl/why-weight-decay. | [
"Maksym Andriushchenko",
"Francesco D'Angelo",
"Aditya Varre",
"Nicolas Flammarion"
] | 2023-10-06 17:58:21 | http://arxiv.org/abs/2310.04415v1 | http://arxiv.org/pdf/2310.04415v1 | 2310.04415v1 |
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets | Offline policy learning is aimed at learning decision-making policies using
existing datasets of trajectories without collecting additional data. The
primary motivation for using reinforcement learning (RL) instead of supervised
learning techniques such as behavior cloning is to find a policy that achieves
a higher average return than the trajectories constituting the dataset.
However, we empirically find that when a dataset is dominated by suboptimal
trajectories, state-of-the-art offline RL algorithms do not substantially
improve over the average return of trajectories in the dataset. We argue this
is due to an assumption made by current offline RL algorithms of staying close
to the trajectories in the dataset. If the dataset primarily consists of
sub-optimal trajectories, this assumption forces the policy to mimic the
suboptimal actions. We overcome this issue by proposing a sampling strategy
that enables the policy to only be constrained to ``good data" rather than all
actions in the dataset (i.e., uniform sampling). We present a realization of
the sampling strategy and an algorithm that can be used as a plug-and-play
module in standard offline RL algorithms. Our evaluation demonstrates
significant performance gains in 72 imbalanced datasets, D4RL dataset, and
across three different offline RL algorithms. Code is available at
https://github.com/Improbable-AI/dw-offline-rl. | [
"Zhang-Wei Hong",
"Aviral Kumar",
"Sathwik Karnik",
"Abhishek Bhandwaldar",
"Akash Srivastava",
"Joni Pajarinen",
"Romain Laroche",
"Abhishek Gupta",
"Pulkit Agrawal"
] | 2023-10-06 17:58:14 | http://arxiv.org/abs/2310.04413v2 | http://arxiv.org/pdf/2310.04413v2 | 2310.04413v2 |
Understanding, Predicting and Better Resolving Q-Value Divergence in Offline-RL | The divergence of the Q-value estimation has been a prominent issue in
offline RL, where the agent has no access to real dynamics. Traditional beliefs
attribute this instability to querying out-of-distribution actions when
bootstrapping value targets. Though this issue can be alleviated with policy
constraints or conservative Q estimation, a theoretical understanding of the
underlying mechanism causing the divergence has been absent. In this work, we
aim to thoroughly comprehend this mechanism and attain an improved solution. We
first identify a fundamental pattern, self-excitation, as the primary cause of
Q-value estimation divergence in offline RL. Then, we propose a novel
Self-Excite Eigenvalue Measure (SEEM) metric based on Neural Tangent Kernel
(NTK) to measure the evolving property of Q-network at training, which provides
an intriguing explanation of the emergence of divergence. For the first time,
our theory can reliably decide whether the training will diverge at an early
stage, and even predict the order of the growth for the estimated Q-value, the
model's norm, and the crashing step when an SGD optimizer is used. The
experiments demonstrate perfect alignment with this theoretic analysis.
Building on our insights, we propose to resolve divergence from a novel
perspective, namely improving the model's architecture for better extrapolating
behavior. Through extensive empirical studies, we identify LayerNorm as a good
solution to effectively avoid divergence without introducing detrimental bias,
leading to superior performance. Experimental results prove that it can still
work in some most challenging settings, i.e. using only 1 transitions of the
dataset, where all previous methods fail. Moreover, it can be easily plugged
into modern offline RL methods and achieve SOTA results on many challenging
tasks. We also give unique insights into its effectiveness. | [
"Yang Yue",
"Rui Lu",
"Bingyi Kang",
"Shiji Song",
"Gao Huang"
] | 2023-10-06 17:57:44 | http://arxiv.org/abs/2310.04411v1 | http://arxiv.org/pdf/2310.04411v1 | 2310.04411v1 |
Policy-Gradient Training of Language Models for Ranking | Text retrieval plays a crucial role in incorporating factual knowledge for
decision making into language processing pipelines, ranging from chat-based web
search to question answering systems. Current state-of-the-art text retrieval
models leverage pre-trained large language models (LLMs) to achieve competitive
performance, but training LLM-based retrievers via typical contrastive losses
requires intricate heuristics, including selecting hard negatives and using
additional supervision as learning signals. This reliance on heuristics stems
from the fact that the contrastive loss itself is heuristic and does not
directly optimize the downstream metrics of decision quality at the end of the
processing pipeline. To address this issue, we introduce Neural PG-RANK, a
novel training algorithm that learns to rank by instantiating a LLM as a
Plackett-Luce ranking policy. Neural PG-RANK provides a principled method for
end-to-end training of retrieval models as part of larger decision systems via
policy gradient, with little reliance on complex heuristics, and it effectively
unifies the training objective with downstream decision-making quality. We
conduct extensive experiments on various text retrieval benchmarks. The results
demonstrate that when the training objective aligns with the evaluation setup,
Neural PG-RANK yields remarkable in-domain performance improvement, with
substantial out-of-domain generalization to some critical datasets employed in
downstream question answering tasks. | [
"Ge Gao",
"Jonathan D. Chang",
"Claire Cardie",
"Kianté Brantley",
"Thorsten Joachim"
] | 2023-10-06 17:55:23 | http://arxiv.org/abs/2310.04407v1 | http://arxiv.org/pdf/2310.04407v1 | 2310.04407v1 |
Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models | While large language models (LLMs) have demonstrated impressive performance
on a range of decision-making tasks, they rely on simple acting processes and
fall short of broad deployment as autonomous agents. We introduce LATS
(Language Agent Tree Search), a general framework that synergizes the
capabilities of LLMs in planning, acting, and reasoning. Drawing inspiration
from Monte Carlo tree search in model-based reinforcement learning, LATS
employs LLMs as agents, value functions, and optimizers, repurposing their
latent strengths for enhanced decision-making. What is crucial in this method
is the use of an environment for external feedback, which offers a more
deliberate and adaptive problem-solving mechanism that moves beyond the
limitations of existing techniques. Our experimental evaluation across diverse
domains, such as programming, HotPotQA, and WebShop, illustrates the
applicability of LATS for both reasoning and acting. In particular, LATS
achieves 94.4\% for programming on HumanEval with GPT-4 and an average score of
75.9 for web browsing on WebShop with GPT-3.5, demonstrating the effectiveness
and generality of our method. | [
"Andy Zhou",
"Kai Yan",
"Michal Shlapentokh-Rothman",
"Haohan Wang",
"Yu-Xiong Wang"
] | 2023-10-06 17:55:11 | http://arxiv.org/abs/2310.04406v1 | http://arxiv.org/pdf/2310.04406v1 | 2310.04406v1 |
On the Embedding Collapse when Scaling up Recommendation Models | Recent advances in deep foundation models have led to a promising trend of
developing large recommendation models to leverage vast amounts of available
data. However, we experiment to scale up existing recommendation models and
observe that the enlarged models do not improve satisfactorily. In this
context, we investigate the embedding layers of enlarged models and identify a
phenomenon of embedding collapse, which ultimately hinders scalability, wherein
the embedding matrix tends to reside in a low-dimensional subspace. Through
empirical and theoretical analysis, we demonstrate that the feature interaction
module specific to recommendation models has a two-sided effect. On the one
hand, the interaction restricts embedding learning when interacting with
collapsed embeddings, exacerbating the collapse issue. On the other hand,
feature interaction is crucial in mitigating the fitting of spurious features,
thereby improving scalability. Based on this analysis, we propose a simple yet
effective multi-embedding design incorporating embedding-set-specific
interaction modules to capture diverse patterns and reduce collapse. Extensive
experiments demonstrate that this proposed design provides consistent
scalability for various recommendation models. | [
"Xingzhuo Guo",
"Junwei Pan",
"Ximei Wang",
"Baixu Chen",
"Jie Jiang",
"Mingsheng Long"
] | 2023-10-06 17:50:38 | http://arxiv.org/abs/2310.04400v1 | http://arxiv.org/pdf/2310.04400v1 | 2310.04400v1 |
Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference | We propose a method to improve the efficiency and accuracy of amortized
Bayesian inference (ABI) by leveraging universal symmetries in the
probabilistic joint model $p(\theta, y)$ of parameters $\theta$ and data $y$.
In a nutshell, we invert Bayes' theorem and estimate the marginal likelihood
based on approximate representations of the joint model. Upon perfect
approximation, the marginal likelihood is constant across all parameter values
by definition. However, approximation error leads to undesirable variance in
the marginal likelihood estimates across different parameter values. We
formulate violations of this symmetry as a loss function to accelerate the
learning dynamics of conditional neural density estimators. We apply our method
to a bimodal toy problem with an explicit likelihood (likelihood-based) and a
realistic model with an implicit likelihood (simulation-based). | [
"Marvin Schmitt",
"Daniel Habermann",
"Paul-Christian Bürkner",
"Ullrich Köthe",
"Stefan T. Radev"
] | 2023-10-06 17:41:41 | http://arxiv.org/abs/2310.04395v2 | http://arxiv.org/pdf/2310.04395v2 | 2310.04395v2 |
FMM-Head: Enhancing Autoencoder-based ECG anomaly detection with prior knowledge | Detecting anomalies in electrocardiogram data is crucial to identifying
deviations from normal heartbeat patterns and providing timely intervention to
at-risk patients. Various AutoEncoder models (AE) have been proposed to tackle
the anomaly detection task with ML. However, these models do not consider the
specific patterns of ECG leads and are unexplainable black boxes. In contrast,
we replace the decoding part of the AE with a reconstruction head (namely,
FMM-Head) based on prior knowledge of the ECG shape. Our model consistently
achieves higher anomaly detection capabilities than state-of-the-art models, up
to 0.31 increase in area under the ROC curve (AUROC), with as little as half
the original model size and explainable extracted features. The processing time
of our model is four orders of magnitude lower than solving an optimization
problem to obtain the same parameters, thus making it suitable for real-time
ECG parameters extraction and anomaly detection. | [
"Giacomo Verardo",
"Magnus Boman",
"Samuel Bruchfeld",
"Marco Chiesa",
"Sabine Koch",
"Gerald Q. Maguire Jr.",
"Dejan Kostic"
] | 2023-10-06 17:20:11 | http://arxiv.org/abs/2310.05848v1 | http://arxiv.org/pdf/2310.05848v1 | 2310.05848v1 |
Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference | Latent Diffusion models (LDMs) have achieved remarkable results in
synthesizing high-resolution images. However, the iterative sampling process is
computationally intensive and leads to slow generation. Inspired by Consistency
Models (song et al.), we propose Latent Consistency Models (LCMs), enabling
swift inference with minimal steps on any pre-trained LDMs, including Stable
Diffusion (rombach et al). Viewing the guided reverse diffusion process as
solving an augmented probability flow ODE (PF-ODE), LCMs are designed to
directly predict the solution of such ODE in latent space, mitigating the need
for numerous iterations and allowing rapid, high-fidelity sampling. Efficiently
distilled from pre-trained classifier-free guided diffusion models, a
high-quality 768 x 768 2~4-step LCM takes only 32 A100 GPU hours for training.
Furthermore, we introduce Latent Consistency Fine-tuning (LCF), a novel method
that is tailored for fine-tuning LCMs on customized image datasets. Evaluation
on the LAION-5B-Aesthetics dataset demonstrates that LCMs achieve
state-of-the-art text-to-image generation performance with few-step inference.
Project Page: https://latent-consistency-models.github.io/ | [
"Simian Luo",
"Yiqin Tan",
"Longbo Huang",
"Jian Li",
"Hang Zhao"
] | 2023-10-06 17:11:58 | http://arxiv.org/abs/2310.04378v1 | http://arxiv.org/pdf/2310.04378v1 | 2310.04378v1 |
Confronting Reward Model Overoptimization with Constrained RLHF | Large language models are typically aligned with human preferences by
optimizing $\textit{reward models}$ (RMs) fitted to human feedback. However,
human preferences are multi-faceted, and it is increasingly common to derive
reward from a composition of simpler reward models which each capture a
different aspect of language quality. This itself presents a challenge, as it
is difficult to appropriately weight these component RMs when combining them.
Compounding this difficulty, because any RM is only a proxy for human
evaluation, this process is vulnerable to $\textit{overoptimization}$, wherein
past a certain point, accumulating higher reward is associated with worse human
ratings. In this paper, we perform, to our knowledge, the first study on
overoptimization in composite RMs, showing that correlation between component
RMs has a significant effect on the locations of these points. We then
introduce an approach to solve this issue using constrained reinforcement
learning as a means of preventing the agent from exceeding each RM's threshold
of usefulness. Our method addresses the problem of weighting component RMs by
learning dynamic weights, naturally expressed by Lagrange multipliers. As a
result, each RM stays within the range at which it is an effective proxy,
improving evaluation performance. Finally, we introduce an adaptive method
using gradient-free optimization to identify and optimize towards these points
during a single run. | [
"Ted Moskovitz",
"Aaditya K. Singh",
"DJ Strouse",
"Tuomas Sandholm",
"Ruslan Salakhutdinov",
"Anca D. Dragan",
"Stephen McAleer"
] | 2023-10-06 16:59:17 | http://arxiv.org/abs/2310.04373v2 | http://arxiv.org/pdf/2310.04373v2 | 2310.04373v2 |
MBTFNet: Multi-Band Temporal-Frequency Neural Network For Singing Voice Enhancement | A typical neural speech enhancement (SE) approach mainly handles speech and
noise mixtures, which is not optimal for singing voice enhancement scenarios.
Music source separation (MSS) models treat vocals and various accompaniment
components equally, which may reduce performance compared to the model that
only considers vocal enhancement. In this paper, we propose a novel multi-band
temporal-frequency neural network (MBTFNet) for singing voice enhancement,
which particularly removes background music, noise and even backing vocals from
singing recordings. MBTFNet combines inter and intra-band modeling for better
processing of full-band signals. Dual-path modeling are introduced to expand
the receptive field of the model. We propose an implicit personalized
enhancement (IPE) stage based on signal-to-noise ratio (SNR) estimation, which
further improves the performance of MBTFNet. Experiments show that our proposed
model significantly outperforms several state-of-the-art SE and MSS models. | [
"Weiming Xu",
"Zhouxuan Chen",
"Zhili Tan",
"Shubo Lv",
"Runduo Han",
"Wenjiang Zhou",
"Weifeng Zhao",
"Lei Xie"
] | 2023-10-06 16:44:47 | http://arxiv.org/abs/2310.04369v1 | http://arxiv.org/pdf/2310.04369v1 | 2310.04369v1 |
A Marketplace Price Anomaly Detection System at Scale | Online marketplaces execute large volume of price updates that are initiated
by individual marketplace sellers each day on the platform. This price
democratization comes with increasing challenges with data quality. Lack of
centralized guardrails that are available for a traditional online retailer
causes a higher likelihood for inaccurate prices to get published on the
website, leading to poor customer experience and potential for revenue loss. We
present MoatPlus (Masked Optimal Anchors using Trees, Proximity-based Labeling
and Unsupervised Statistical-features), a scalable price anomaly detection
framework for a growing marketplace platform. The goal is to leverage proximity
and historical price trends from unsupervised statistical features to generate
an upper price bound. We build an ensemble of models to detect irregularities
in price-based features, exclude irregular features and use optimized weighting
scheme to build a reliable price bound in real-time pricing pipeline. We
observed that our approach improves precise anchor coverage by up to 46.6% in
high-vulnerability item subsets | [
"Akshit Sarpal",
"Qiwen Kang",
"Fangping Huang",
"Yang Song",
"Lijie Wan"
] | 2023-10-06 16:41:51 | http://arxiv.org/abs/2310.04367v2 | http://arxiv.org/pdf/2310.04367v2 | 2310.04367v2 |
Amortizing intractable inference in large language models | Autoregressive large language models (LLMs) compress knowledge from their
training data through next-token conditional distributions. This limits
tractable querying of this knowledge to start-to-end autoregressive sampling.
However, many tasks of interest -- including sequence continuation, infilling,
and other forms of constrained generation -- involve sampling from intractable
posterior distributions. We address this limitation by using amortized Bayesian
inference to sample from these intractable posteriors. Such amortization is
algorithmically achieved by fine-tuning LLMs via diversity-seeking
reinforcement learning algorithms: generative flow networks (GFlowNets). We
empirically demonstrate that this distribution-matching paradigm of LLM
fine-tuning can serve as an effective alternative to maximum-likelihood
training and reward-maximizing policy optimization. As an important
application, we interpret chain-of-thought reasoning as a latent variable
modeling problem and demonstrate that our approach enables data-efficient
adaptation of LLMs to tasks that require multi-step rationalization and tool
use. | [
"Edward J. Hu",
"Moksh Jain",
"Eric Elmoznino",
"Younesse Kaddar",
"Guillaume Lajoie",
"Yoshua Bengio",
"Nikolay Malkin"
] | 2023-10-06 16:36:08 | http://arxiv.org/abs/2310.04363v1 | http://arxiv.org/pdf/2310.04363v1 | 2310.04363v1 |
Exploiting Transformer Activation Sparsity with Dynamic Inference | Transformer models, despite their impressive performance, often face
practical limitations due to their high computational requirements. At the same
time, previous studies have revealed significant activation sparsity in these
models, indicating the presence of redundant computations. In this paper, we
propose Dynamic Sparsified Transformer Inference (DSTI), a method that
radically reduces the inference cost of Transformer models by enforcing
activation sparsity and subsequently transforming a dense model into its sparse
Mixture of Experts (MoE) version. We demonstrate that it is possible to train
small gating networks that successfully predict the relative contribution of
each expert during inference. Furthermore, we introduce a mechanism that
dynamically determines the number of executed experts individually for each
token. DSTI can be applied to any Transformer-based architecture and has
negligible impact on the accuracy. For the BERT-base classification model, we
reduce inference cost by almost 60%. | [
"Mikołaj Piórczyński",
"Filip Szatkowski",
"Klaudia Bałazy",
"Bartosz Wójcik"
] | 2023-10-06 16:34:51 | http://arxiv.org/abs/2310.04361v1 | http://arxiv.org/pdf/2310.04361v1 | 2310.04361v1 |
Integrating Transformations in Probabilistic Circuits | This study addresses the predictive limitation of probabilistic circuits and
introduces transformations as a remedy to overcome it. We demonstrate this
limitation in robotic scenarios. We motivate that independent component
analysis is a sound tool to preserve the independence properties of
probabilistic circuits. Our approach is an extension of joint probability
trees, which are model-free deterministic circuits. By doing so, it is
demonstrated that the proposed approach is able to achieve higher likelihoods
while using fewer parameters compared to the joint probability trees on seven
benchmark data sets as well as on real robot data. Furthermore, we discuss how
to integrate transformations into tree-based learning routines. Finally, we
argue that exact inference with transformed quantile parameterized
distributions is not tractable. However, our approach allows for efficient
sampling and approximate inference. | [
"Tom Schierenbeck",
"Vladimir Vutov",
"Thorsten Dickhaus",
"Michael Beetz"
] | 2023-10-06 16:23:09 | http://arxiv.org/abs/2310.04354v1 | http://arxiv.org/pdf/2310.04354v1 | 2310.04354v1 |
A Language-Agent Approach to Formal Theorem-Proving | Language agents, which use a large language model (LLM) capable of in-context
learning to interact with an external environment, have recently emerged as a
promising approach to control tasks. We present the first language-agent
approach to formal theorem-proving. Our method, COPRA, uses a high-capacity,
black-box LLM (GPT-4) as part of a policy for a stateful backtracking search.
During the search, the policy can select proof tactics and retrieve lemmas and
definitions from an external database. Each selected tactic is executed in the
underlying proof framework, and the execution feedback is used to build the
prompt for the next policy invocation. The search also tracks selected
information from its history and uses it to reduce hallucinations and
unnecessary LLM queries.
We evaluate COPRA on the miniF2F benchmark for Lean and a set of Coq tasks
from the Compcert project. On these benchmarks, COPRA is significantly better
than one-shot invocations of GPT-4, as well as state-of-the-art models
fine-tuned on proof data, at finding correct proofs quickly. | [
"Amitayush Thakur",
"Yeming Wen",
"Swarat Chaudhuri"
] | 2023-10-06 16:21:22 | http://arxiv.org/abs/2310.04353v1 | http://arxiv.org/pdf/2310.04353v1 | 2310.04353v1 |
Fair Feature Importance Scores for Interpreting Tree-Based Methods and Surrogates | Across various sectors such as healthcare, criminal justice, national
security, finance, and technology, large-scale machine learning (ML) and
artificial intelligence (AI) systems are being deployed to make critical
data-driven decisions. Many have asked if we can and should trust these ML
systems to be making these decisions. Two critical components are prerequisites
for trust in ML systems: interpretability, or the ability to understand why the
ML system makes the decisions it does, and fairness, which ensures that ML
systems do not exhibit bias against certain individuals or groups. Both
interpretability and fairness are important and have separately received
abundant attention in the ML literature, but so far, there have been very few
methods developed to directly interpret models with regard to their fairness.
In this paper, we focus on arguably the most popular type of ML interpretation:
feature importance scores. Inspired by the use of decision trees in knowledge
distillation, we propose to leverage trees as interpretable surrogates for
complex black-box ML models. Specifically, we develop a novel fair feature
importance score for trees that can be used to interpret how each feature
contributes to fairness or bias in trees, tree-based ensembles, or tree-based
surrogates of any complex ML system. Like the popular mean decrease in impurity
for trees, our Fair Feature Importance Score is defined based on the mean
decrease (or increase) in group bias. Through simulations as well as real
examples on benchmark fairness datasets, we demonstrate that our Fair Feature
Importance Score offers valid interpretations for both tree-based ensembles and
tree-based surrogates of other ML systems. | [
"Camille Olivia Little",
"Debolina Halder Lina",
"Genevera I. Allen"
] | 2023-10-06 16:21:21 | http://arxiv.org/abs/2310.04352v1 | http://arxiv.org/pdf/2310.04352v1 | 2310.04352v1 |
Learning to Grasp: from Somewhere to Anywhere | Robotic grasping is still a partially solved, multidisciplinary problem where
data-driven techniques play an increasing role. The sparse nature of rewards
make the automatic generation of grasping datasets challenging, especially for
unconventional morphologies or highly actuated end-effectors. Most approaches
for obtaining large-scale datasets rely on numerous human-provided
demonstrations or heavily engineered solutions that do not scale well. Recent
advances in Quality-Diversity (QD) methods have investigated how to learn
object grasping at a specific pose with different robot morphologies. The
present work introduces a pipeline for adapting QD-generated trajectories to
new object poses. Using an RGB-D data stream, the vision pipeline first detects
the targeted object, predicts its 6-DOF pose, and finally tracks it. An
automatically generated reach-and-grasp trajectory can then be adapted by
projecting it relatively to the object frame. Hundreds of trajectories have
been deployed into the real world on several objects and with different robotic
setups: a Franka Research 3 with a parallel gripper and a UR5 with a dexterous
SIH Schunk hand. The transfer ratio obtained when applying transformation to
the object pose matches the one obtained when the object pose matches the
simulation, demonstrating the efficiency of the proposed approach. | [
"François Hélénon",
"Johann Huber",
"Faïz Ben Amar",
"Stéphane Doncieux"
] | 2023-10-06 16:16:00 | http://arxiv.org/abs/2310.04349v1 | http://arxiv.org/pdf/2310.04349v1 | 2310.04349v1 |
Neur2RO: Neural Two-Stage Robust Optimization | Robust optimization provides a mathematical framework for modeling and
solving decision-making problems under worst-case uncertainty. This work
addresses two-stage robust optimization (2RO) problems (also called adjustable
robust optimization), wherein first-stage and second-stage decisions are made
before and after uncertainty is realized, respectively. This results in a
nested min-max-min optimization problem which is extremely challenging
computationally, especially when the decisions are discrete. We propose
Neur2RO, an efficient machine learning-driven instantiation of
column-and-constraint generation (CCG), a classical iterative algorithm for
2RO. Specifically, we learn to estimate the value function of the second-stage
problem via a novel neural network architecture that is easy to optimize over
by design. Embedding our neural network into CCG yields high-quality solutions
quickly as evidenced by experiments on two 2RO benchmarks, knapsack and capital
budgeting. For knapsack, Neur2RO finds solutions that are within roughly $2\%$
of the best-known values in a few seconds compared to the three hours of the
state-of-the-art exact branch-and-price algorithm; for larger and more complex
instances, Neur2RO finds even better solutions. For capital budgeting, Neur2RO
outperforms three variants of the $k$-adaptability algorithm, particularly on
the largest instances, with a 5 to 10-fold reduction in solution time. Our code
and data are available at https://github.com/khalil-research/Neur2RO. | [
"Justin Dumouchelle",
"Esther Julien",
"Jannis Kurtz",
"Elias B. Khalil"
] | 2023-10-06 16:11:46 | http://arxiv.org/abs/2310.04345v1 | http://arxiv.org/pdf/2310.04345v1 | 2310.04345v1 |
Functional Geometry Guided Protein Sequence and Backbone Structure Co-Design | Proteins are macromolecules responsible for essential functions in almost all
living organisms. Designing reasonable proteins with desired functions is
crucial. A protein's sequence and structure are strongly correlated and they
together determine its function. In this paper, we propose NAEPro, a model to
jointly design Protein sequence and structure based on automatically detected
functional sites. NAEPro is powered by an interleaving network of attention and
equivariant layers, which can capture global correlation in a whole sequence
and local influence from nearest amino acids in three dimensional (3D) space.
Such an architecture facilitates effective yet economic message passing at two
levels. We evaluate our model and several strong baselines on two protein
datasets, $\beta$-lactamase and myoglobin. Experimental results show that our
model consistently achieves the highest amino acid recovery rate, TM-score, and
the lowest RMSD among all competitors. These findings prove the capability of
our model to design protein sequences and structures that closely resemble
their natural counterparts. Furthermore, in-depth analysis further confirms our
model's ability to generate highly effective proteins capable of binding to
their target metallocofactors. We provide code, data and models in Github. | [
"Zhenqiao Song",
"Yunlong Zhao",
"Wenxian Shi",
"Yang Yang",
"Lei Li"
] | 2023-10-06 16:08:41 | http://arxiv.org/abs/2310.04343v2 | http://arxiv.org/pdf/2310.04343v2 | 2310.04343v2 |
Applying Reinforcement Learning to Option Pricing and Hedging | This thesis provides an overview of the recent advances in reinforcement
learning in pricing and hedging financial instruments, with a primary focus on
a detailed explanation of the Q-Learning Black Scholes approach, introduced by
Halperin (2017). This reinforcement learning approach bridges the traditional
Black and Scholes (1973) model with novel artificial intelligence algorithms,
enabling option pricing and hedging in a completely model-free and data-driven
way. This paper also explores the algorithm's performance under different state
variables and scenarios for a European put option. The results reveal that the
model is an accurate estimator under different levels of volatility and hedging
frequency. Moreover, this method exhibits robust performance across various
levels of option's moneyness. Lastly, the algorithm incorporates proportional
transaction costs, indicating diverse impacts on profit and loss, affected by
different statistical properties of the state variables. | [
"Zoran Stoiljkovic"
] | 2023-10-06 15:59:12 | http://arxiv.org/abs/2310.04336v1 | http://arxiv.org/pdf/2310.04336v1 | 2310.04336v1 |
Saliency-Guided Hidden Associative Replay for Continual Learning | Continual Learning is a burgeoning domain in next-generation AI, focusing on
training neural networks over a sequence of tasks akin to human learning. While
CL provides an edge over traditional supervised learning, its central challenge
remains to counteract catastrophic forgetting and ensure the retention of prior
tasks during subsequent learning. Amongst various strategies to tackle this,
replay based methods have emerged as preeminent, echoing biological memory
mechanisms. However, these methods are memory intensive, often preserving
entire data samples, an approach inconsistent with humans selective memory
retention of salient experiences. While some recent works have explored the
storage of only significant portions of data in episodic memory, the inherent
nature of partial data necessitates innovative retrieval mechanisms. Current
solutions, like inpainting, approximate full data reconstruction from partial
cues, a method that diverges from genuine human memory processes. Addressing
these nuances, this paper presents the Saliency Guided Hidden Associative
Replay for Continual Learning. This novel framework synergizes associative
memory with replay-based strategies. SHARC primarily archives salient data
segments via sparse memory encoding. Importantly, by harnessing associative
memory paradigms, it introduces a content focused memory retrieval mechanism,
promising swift and near-perfect recall, bringing CL a step closer to authentic
human memory processes. Extensive experimental results demonstrate the
effectiveness of our proposed method for various continual learning tasks. | [
"Guangji Bai",
"Qilong Zhao",
"Xiaoyang Jiang",
"Yifei Zhang",
"Liang Zhao"
] | 2023-10-06 15:54:12 | http://arxiv.org/abs/2310.04334v1 | http://arxiv.org/pdf/2310.04334v1 | 2310.04334v1 |
T-Rep: Representation Learning for Time Series using Time-Embeddings | Multivariate time series present challenges to standard machine learning
techniques, as they are often unlabeled, high dimensional, noisy, and contain
missing data. To address this, we propose T-Rep, a self-supervised method to
learn time series representations at a timestep granularity. T-Rep learns
vector embeddings of time alongside its feature extractor, to extract temporal
features such as trend, periodicity, or distribution shifts from the signal.
These time-embeddings are leveraged in pretext tasks, to incorporate smooth and
fine-grained temporal dependencies in the representations, as well as reinforce
robustness to missing data. We evaluate T-Rep on downstream classification,
forecasting, and anomaly detection tasks. It is compared to existing
self-supervised algorithms for time series, which it outperforms in all three
tasks. We test T-Rep in missing data regimes, where it proves more resilient
than its counterparts. Finally, we provide latent space visualisation
experiments, highlighting the interpretability of the learned representations. | [
"Archibald Fraikin",
"Adrien Bennetot",
"Stéphanie Allassonnière"
] | 2023-10-06 15:45:28 | http://arxiv.org/abs/2310.04486v1 | http://arxiv.org/pdf/2310.04486v1 | 2310.04486v1 |
Robust Losses for Decision-Focused Learning | Optimization models used to make discrete decisions often contain uncertain
parameters that are context-dependent and are estimated through prediction. To
account for the quality of the decision made based on the prediction,
decision-focused learning (end-to-end predict-then-optimize) aims at training
the predictive model to minimize regret, i.e., the loss incurred by making a
suboptimal decision. Despite the challenge of this loss function being possibly
non-convex and in general non-differentiable, effective gradient-based learning
approaches have been proposed to minimize the expected loss, using the
empirical loss as a surrogate. However, empirical regret can be an ineffective
surrogate because the uncertainty in the optimization model makes the empirical
regret unequal to the expected regret in expectation. To illustrate the impact
of this inequality, we evaluate the effect of aleatoric and epistemic
uncertainty on the accuracy of empirical regret as a surrogate. Next, we
propose three robust loss functions that more closely approximate expected
regret. Experimental results show that training two state-of-the-art
decision-focused learning approaches using robust regret losses improves
test-sample empirical regret in general while keeping computational time
equivalent relative to the number of training epochs. | [
"Noah Schutte",
"Krzysztof Postek",
"Neil Yorke-Smith"
] | 2023-10-06 15:45:10 | http://arxiv.org/abs/2310.04328v1 | http://arxiv.org/pdf/2310.04328v1 | 2310.04328v1 |
Program Synthesis with Best-First Bottom-Up Search | Cost-guided bottom-up search (BUS) algorithms use a cost function to guide
the search to solve program synthesis tasks. In this paper, we show that
current state-of-the-art cost-guided BUS algorithms suffer from a common
problem: they can lose useful information given by the model and fail to
perform the search in a best-first order according to a cost function. We
introduce a novel best-first bottom-up search algorithm, which we call Bee
Search, that does not suffer information loss and is able to perform
cost-guided bottom-up synthesis in a best-first manner. Importantly, Bee Search
performs best-first search with respect to the generation of programs, i.e., it
does not even create in memory programs that are more expensive than the
solution program. It attains best-first ordering with respect to generation by
performing a search in an abstract space of program costs. We also introduce a
new cost function that better uses the information provided by an existing cost
model. Empirical results on string manipulation and bit-vector tasks show that
Bee Search can outperform existing cost-guided BUS approaches when employing
more complex domain-specific languages (DSLs); Bee Search and previous
approaches perform equally well with simpler DSLs. Furthermore, our new cost
function with Bee Search outperforms previous cost functions on string
manipulation tasks. | [
"Saqib Ameen",
"Levi H. S. Lelis"
] | 2023-10-06 15:44:47 | http://arxiv.org/abs/2310.04327v1 | http://arxiv.org/pdf/2310.04327v1 | 2310.04327v1 |
Adjustable Robust Reinforcement Learning for Online 3D Bin Packing | Designing effective policies for the online 3D bin packing problem (3D-BPP)
has been a long-standing challenge, primarily due to the unpredictable nature
of incoming box sequences and stringent physical constraints. While current
deep reinforcement learning (DRL) methods for online 3D-BPP have shown
promising results in optimizing average performance over an underlying box
sequence distribution, they often fail in real-world settings where some
worst-case scenarios can materialize. Standard robust DRL algorithms tend to
overly prioritize optimizing the worst-case performance at the expense of
performance under normal problem instance distribution. To address these
issues, we first introduce a permutation-based attacker to investigate the
practical robustness of both DRL-based and heuristic methods proposed for
solving online 3D-BPP. Then, we propose an adjustable robust reinforcement
learning (AR2L) framework that allows efficient adjustment of robustness
weights to achieve the desired balance of the policy's performance in average
and worst-case environments. Specifically, we formulate the objective function
as a weighted sum of expected and worst-case returns, and derive the lower
performance bound by relating to the return under a mixture dynamics. To
realize this lower bound, we adopt an iterative procedure that searches for the
associated mixture dynamics and improves the corresponding policy. We integrate
this procedure into two popular robust adversarial algorithms to develop the
exact and approximate AR2L algorithms. Experiments demonstrate that AR2L is
versatile in the sense that it improves policy robustness while maintaining an
acceptable level of performance for the nominal case. | [
"Yuxin Pan",
"Yize Chen",
"Fangzhen Lin"
] | 2023-10-06 15:34:21 | http://arxiv.org/abs/2310.04323v1 | http://arxiv.org/pdf/2310.04323v1 | 2310.04323v1 |
Latent Graph Inference with Limited Supervision | Latent graph inference (LGI) aims to jointly learn the underlying graph
structure and node representations from data features. However, existing LGI
methods commonly suffer from the issue of supervision starvation, where massive
edge weights are learned without semantic supervision and do not contribute to
the training loss. Consequently, these supervision-starved weights, which may
determine the predictions of testing samples, cannot be semantically optimal,
resulting in poor generalization. In this paper, we observe that this issue is
actually caused by the graph sparsification operation, which severely destroys
the important connections established between pivotal nodes and labeled ones.
To address this, we propose to restore the corrupted affinities and replenish
the missed supervision for better LGI. The key challenge then lies in
identifying the critical nodes and recovering the corrupted affinities. We
begin by defining the pivotal nodes as $k$-hop starved nodes, which can be
identified based on a given adjacency matrix. Considering the high
computational burden, we further present a more efficient alternative inspired
by CUR matrix decomposition. Subsequently, we eliminate the starved nodes by
reconstructing the destroyed connections. Extensive experiments on
representative benchmarks demonstrate that reducing the starved nodes
consistently improves the performance of state-of-the-art LGI methods,
especially under extremely limited supervision (6.12% improvement on Pubmed
with a labeling rate of only 0.3%). | [
"Jianglin Lu",
"Yi Xu",
"Huan Wang",
"Yue Bai",
"Yun Fu"
] | 2023-10-06 15:22:40 | http://arxiv.org/abs/2310.04314v1 | http://arxiv.org/pdf/2310.04314v1 | 2310.04314v1 |
Distributed Deep Joint Source-Channel Coding with Decoder-Only Side Information | We consider low-latency image transmission over a noisy wireless channel when
correlated side information is present only at the receiver side (the Wyner-Ziv
scenario). In particular, we are interested in developing practical schemes
using a data-driven joint source-channel coding (JSCC) approach, which has been
previously shown to outperform conventional separation-based approaches in the
practical finite blocklength regimes, and to provide graceful degradation with
channel quality. We propose a novel neural network architecture that
incorporates the decoder-only side information at multiple stages at the
receiver side. Our results demonstrate that the proposed method succeeds in
integrating the side information, yielding improved performance at all channel
noise levels in terms of the various distortion criteria considered here,
especially at low channel signal-to-noise ratios (SNRs) and small bandwidth
ratios (BRs). We also provide the source code of the proposed method to enable
further research and reproducibility of the results. | [
"Selim F. Yilmaz",
"Ezgi Ozyilkan",
"Deniz Gunduz",
"Elza Erkip"
] | 2023-10-06 15:17:45 | http://arxiv.org/abs/2310.04311v1 | http://arxiv.org/pdf/2310.04311v1 | 2310.04311v1 |
Convergent ADMM Plug and Play PET Image Reconstruction | In this work, we investigate hybrid PET reconstruction algorithms based on
coupling a model-based variational reconstruction and the application of a
separately learnt Deep Neural Network operator (DNN) in an ADMM Plug and Play
framework. Following recent results in optimization, fixed point convergence of
the scheme can be achieved by enforcing an additional constraint on network
parameters during learning. We propose such an ADMM algorithm and show in a
realistic [18F]-FDG synthetic brain exam that the proposed scheme indeed lead
experimentally to convergence to a meaningful fixed point. When the proposed
constraint is not enforced during learning of the DNN, the proposed ADMM
algorithm was observed experimentally not to converge. | [
"Florent Sureau",
"Mahdi Latreche",
"Marion Savanier",
"Claude Comtat"
] | 2023-10-06 15:01:32 | http://arxiv.org/abs/2310.04299v1 | http://arxiv.org/pdf/2310.04299v1 | 2310.04299v1 |
Identifying Representations for Intervention Extrapolation | The premise of identifiable and causal representation learning is to improve
the current representation learning paradigm in terms of generalizability or
robustness. Despite recent progress in questions of identifiability, more
theoretical results demonstrating concrete advantages of these methods for
downstream tasks are needed. In this paper, we consider the task of
intervention extrapolation: predicting how interventions affect an outcome,
even when those interventions are not observed at training time, and show that
identifiable representations can provide an effective solution to this task
even if the interventions affect the outcome non-linearly. Our setup includes
an outcome Y, observed features X, which are generated as a non-linear
transformation of latent features Z, and exogenous action variables A, which
influence Z. The objective of intervention extrapolation is to predict how
interventions on A that lie outside the training support of A affect Y. Here,
extrapolation becomes possible if the effect of A on Z is linear and the
residual when regressing Z on A has full support. As Z is latent, we combine
the task of intervention extrapolation with identifiable representation
learning, which we call Rep4Ex: we aim to map the observed features X into a
subspace that allows for non-linear extrapolation in A. We show using Wiener's
Tauberian theorem that the hidden representation is identifiable up to an
affine transformation in Z-space, which is sufficient for intervention
extrapolation. The identifiability is characterized by a novel constraint
describing the linearity assumption of A on Z. Based on this insight, we
propose a method that enforces the linear invariance constraint and can be
combined with any type of autoencoder. We validate our theoretical findings
through synthetic experiments and show that our approach succeeds in predicting
the effects of unseen interventions. | [
"Sorawit Saengkyongam",
"Elan Rosenfeld",
"Pradeep Ravikumar",
"Niklas Pfister",
"Jonas Peters"
] | 2023-10-06 14:58:28 | http://arxiv.org/abs/2310.04295v1 | http://arxiv.org/pdf/2310.04295v1 | 2310.04295v1 |
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets | Recently, pre-trained foundation models have enabled significant advancements
in multiple fields. In molecular machine learning, however, where datasets are
often hand-curated, and hence typically small, the lack of datasets with
labeled features, and codebases to manage those datasets, has hindered the
development of foundation models. In this work, we present seven novel datasets
categorized by size into three distinct categories: ToyMix, LargeMix and
UltraLarge. These datasets push the boundaries in both the scale and the
diversity of supervised labels for molecular learning. They cover nearly 100
million molecules and over 3000 sparsely defined tasks, totaling more than 13
billion individual labels of both quantum and biological nature. In comparison,
our datasets contain 300 times more data points than the widely used OGB-LSC
PCQM4Mv2 dataset, and 13 times more than the quantum-only QM1B dataset. In
addition, to support the development of foundational models based on our
proposed datasets, we present the Graphium graph machine learning library which
simplifies the process of building and training molecular machine learning
models for multi-task and multi-level molecular datasets. Finally, we present a
range of baseline results as a starting point of multi-task and multi-level
training on these datasets. Empirically, we observe that performance on
low-resource biological datasets show improvement by also training on large
amounts of quantum data. This indicates that there may be potential in
multi-task and multi-level training of a foundation model and fine-tuning it to
resource-constrained downstream tasks. | [
"Dominique Beaini",
"Shenyang Huang",
"Joao Alex Cunha",
"Zhiyi Li",
"Gabriela Moisescu-Pareja",
"Oleksandr Dymov",
"Samuel Maddrell-Mander",
"Callum McLean",
"Frederik Wenkel",
"Luis Müller",
"Jama Hussein Mohamud",
"Ali Parviz",
"Michael Craig",
"Michał Koziarski",
"Jiarui Lu",
"Zhaocheng Zhu",
"Cristian Gabellini",
"Kerstin Klaser",
"Josef Dean",
"Cas Wognum",
"Maciej Sypetkowski",
"Guillaume Rabusseau",
"Reihaneh Rabbany",
"Jian Tang",
"Christopher Morris",
"Ioannis Koutis",
"Mirco Ravanelli",
"Guy Wolf",
"Prudencio Tossou",
"Hadrien Mary",
"Therence Bois",
"Andrew Fitzgibbon",
"Błażej Banaszewski",
"Chad Martin",
"Dominic Masters"
] | 2023-10-06 14:51:17 | http://arxiv.org/abs/2310.04292v3 | http://arxiv.org/pdf/2310.04292v3 | 2310.04292v3 |
Assessing Robustness via Score-Based Adversarial Image Generation | Most adversarial attacks and defenses focus on perturbations within small
$\ell_p$-norm constraints. However, $\ell_p$ threat models cannot capture all
relevant semantic-preserving perturbations, and hence, the scope of robustness
evaluations is limited. In this work, we introduce Score-Based Adversarial
Generation (ScoreAG), a novel framework that leverages the advancements in
score-based generative models to generate adversarial examples beyond
$\ell_p$-norm constraints, so-called unrestricted adversarial examples,
overcoming their limitations. Unlike traditional methods, ScoreAG maintains the
core semantics of images while generating realistic adversarial examples,
either by transforming existing images or synthesizing new ones entirely from
scratch. We further exploit the generative capability of ScoreAG to purify
images, empirically enhancing the robustness of classifiers. Our extensive
empirical evaluation demonstrates that ScoreAG matches the performance of
state-of-the-art attacks and defenses across multiple benchmarks. This work
highlights the importance of investigating adversarial examples bounded by
semantics rather than $\ell_p$-norm constraints. ScoreAG represents an
important step towards more encompassing robustness assessments. | [
"Marcel Kollovieh",
"Lukas Gosch",
"Yan Scholten",
"Marten Lienen",
"Stephan Günnemann"
] | 2023-10-06 14:37:22 | http://arxiv.org/abs/2310.04285v1 | http://arxiv.org/pdf/2310.04285v1 | 2310.04285v1 |
On the Error-Propagation of Inexact Deflation for Principal Component Analysis | Principal Component Analysis (PCA) is a popular tool in data analysis,
especially when the data is high-dimensional. PCA aims to find subspaces,
spanned by the so-called \textit{principal components}, that best explain the
variance in the dataset. The deflation method is a popular meta-algorithm --
used to discover such subspaces -- that sequentially finds individual principal
components, starting from the most important one and working its way towards
the less important ones. However, due to its sequential nature, the numerical
error introduced by not estimating principal components exactly -- e.g., due to
numerical approximations through this process -- propagates, as deflation
proceeds. To the best of our knowledge, this is the first work that
mathematically characterizes the error propagation of the inexact deflation
method, and this is the key contribution of this paper. We provide two main
results: $i)$ when the sub-routine for finding the leading eigenvector is
generic, and $ii)$ when power iteration is used as the sub-routine. In the
latter case, the additional directional information from power iteration allows
us to obtain a tighter error bound than the analysis of the sub-routine
agnostic case. As an outcome, we provide explicit characterization on how the
error progresses and affects subsequent principal component estimations for
this fundamental problem. | [
"Fangshuo Liao",
"Junhyung Lyle Kim",
"Cruz Barnum",
"Anastasios Kyrillidis"
] | 2023-10-06 14:33:21 | http://arxiv.org/abs/2310.04283v1 | http://arxiv.org/pdf/2310.04283v1 | 2310.04283v1 |
Deep learning modelling of tip clearance variations on multi-stage axial compressors aerodynamics | Application of deep learning methods to physical simulations such as CFD
(Computational Fluid Dynamics) for turbomachinery applications, have been so
far of limited industrial relevance. This paper demonstrates the development
and application of a deep learning framework for real-time predictions of the
impact of tip clearance variations on the flow field and aerodynamic
performance of multi-stage axial compressors in gas turbines. The proposed
architecture is proven to be scalable to industrial applications, and achieves
in real-time accuracy comparable to the CFD benchmark. The deployed model, is
readily integrated within the manufacturing and build process of gas turbines,
thus providing the opportunity to analytically assess the impact on performance
and potentially reduce requirements for expensive physical tests. | [
"Giuseppe Bruni",
"Sepehr Maleki",
"Senthil K. Krishnababu"
] | 2023-10-06 14:11:21 | http://arxiv.org/abs/2310.04264v2 | http://arxiv.org/pdf/2310.04264v2 | 2310.04264v2 |
Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison | Real-world reinforcement learning (RL) environments, whether in robotics or
industrial settings, often involve non-visual observations and require not only
efficient but also reliable and thus interpretable and flexible RL approaches.
To improve efficiency, agents that perform state representation learning with
auxiliary tasks have been widely studied in visual observation contexts.
However, for real-world problems, dedicated representation learning modules
that are decoupled from RL agents are more suited to meet requirements. This
study compares common auxiliary tasks based on, to the best of our knowledge,
the only decoupled representation learning method for low-dimensional
non-visual observations. We evaluate potential improvements in sample
efficiency and returns for environments ranging from a simple pendulum to a
complex simulated robotics task. Our findings show that representation learning
with auxiliary tasks only provides performance gains in sufficiently complex
environments and that learning environment dynamics is preferable to predicting
rewards. These insights can inform future development of interpretable
representation learning approaches for non-visual observations and advance the
use of RL solutions in real-world scenarios. | [
"Moritz Lange",
"Noah Krystiniak",
"Raphael C. Engelhardt",
"Wolfgang Konen",
"Laurenz Wiskott"
] | 2023-10-06 13:22:26 | http://arxiv.org/abs/2310.04241v2 | http://arxiv.org/pdf/2310.04241v2 | 2310.04241v2 |
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