title
stringlengths 9
208
| abstract
stringlengths 280
2.36k
| authors
sequence | published
stringlengths 19
19
| url
stringlengths 33
33
| pdf_url
stringlengths 33
33
| arxiv_id
stringlengths 12
12
|
|---|---|---|---|---|---|---|
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks | Pre-training on large-scale datasets and then fine-tuning on downstream tasks
have become a standard practice in deep learning. However, pre-training data
often contain label noise that may adversely affect the generalization of the
model. This paper aims to understand the nature of noise in pre-training
datasets and to mitigate its impact on downstream tasks. More specifically,
through extensive experiments of supervised pre-training models on synthetic
noisy ImageNet-1K and YFCC15M datasets, we demonstrate that while slight noise
in pre-training can benefit in-domain (ID) transfer performance, where the
training and testing data share the same distribution, it always deteriorates
out-of-domain (OOD) performance, where training and testing data distribution
are different. We empirically verify that the reason behind is noise in
pre-training shapes the feature space differently. We then propose a
lightweight black-box tuning method (NMTune) to affine the feature space to
mitigate the malignant effect of noise and improve generalization on both ID
and OOD tasks, considering one may not be able to fully fine-tune or even
access the pre-trained models. We conduct practical experiments on popular
vision and language models that are pre-trained on noisy data for evaluation of
our approach. Our analysis and results show the importance of this interesting
and novel research direction, which we term Noisy Model Learning. | [
"Hao Chen",
"Jindong Wang",
"Ankit Shah",
"Ran Tao",
"Hongxin Wei",
"Xing Xie",
"Masashi Sugiyama",
"Bhiksha Raj"
] | 2023-09-29 06:18:15 | http://arxiv.org/abs/2309.17002v1 | http://arxiv.org/pdf/2309.17002v1 | 2309.17002v1 |
A Closer Look at Bearing Fault Classification Approaches | Rolling bearing fault diagnosis has garnered increased attention in recent
years owing to its presence in rotating machinery across various industries,
and an ever increasing demand for efficient operations. Prompt detection and
accurate prediction of bearing failures can help reduce the likelihood of
unexpected machine downtime and enhance maintenance schedules, averting lost
productivity. Recent technological advances have enabled monitoring the health
of these assets at scale using a variety of sensors, and predicting the
failures using modern Machine Learning (ML) approaches including deep learning
architectures. Vibration data has been collected using accelerated
run-to-failure of overloaded bearings, or by introducing known failure in
bearings, under a variety of operating conditions such as rotating speed, load
on the bearing, type of bearing fault, and data acquisition frequency. However,
in the development of bearing failure classification models using vibration
data there is a lack of consensus in the metrics used to evaluate the models,
data partitions used to evaluate models, and methods used to generate failure
labels in run-to-failure experiments. An understanding of the impact of these
choices is important to reliably develop models, and deploy them in practical
settings. In this work, we demonstrate the significance of these choices on the
performance of the models using publicly-available vibration datasets, and
suggest model development considerations for real world scenarios. Our
experimental findings demonstrate that assigning vibration data from a given
bearing across training and evaluation splits leads to over-optimistic
performance estimates, PCA-based approach is able to robustly generate labels
for failure classification in run-to-failure experiments, and $F$ scores are
more insightful to evaluate the models with unbalanced real-world failure data. | [
"Harika Abburi",
"Tanya Chaudhary",
"Haider Ilyas",
"Lakshmi Manne",
"Deepak Mittal",
"Don Williams",
"Derek Snaidauf",
"Edward Bowen",
"Balaji Veeramani"
] | 2023-09-29 06:11:11 | http://arxiv.org/abs/2309.17001v1 | http://arxiv.org/pdf/2309.17001v1 | 2309.17001v1 |
Segment Anything Model is a Good Teacher for Local Feature Learning | Local feature detection and description play an important role in many
computer vision tasks, which are designed to detect and describe keypoints in
"any scene" and "any downstream task". Data-driven local feature learning
methods need to rely on pixel-level correspondence for training, which is
challenging to acquire at scale, thus hindering further improvements in
performance. In this paper, we propose SAMFeat to introduce SAM (segment
anything model), a fundamental model trained on 11 million images, as a teacher
to guide local feature learning and thus inspire higher performance on limited
datasets. To do so, first, we construct an auxiliary task of Pixel Semantic
Relational Distillation (PSRD), which distillates feature relations with
category-agnostic semantic information learned by the SAM encoder into a local
feature learning network, to improve local feature description using semantic
discrimination. Second, we develop a technique called Weakly Supervised
Contrastive Learning Based on Semantic Grouping (WSC), which utilizes semantic
groupings derived from SAM as weakly supervised signals, to optimize the metric
space of local descriptors. Third, we design an Edge Attention Guidance (EAG)
to further improve the accuracy of local feature detection and description by
prompting the network to pay more attention to the edge region guided by SAM.
SAMFeat's performance on various tasks such as image matching on HPatches, and
long-term visual localization on Aachen Day-Night showcases its superiority
over previous local features. The release code is available at
https://github.com/vignywang/SAMFeat. | [
"Jingqian Wu",
"Rongtao Xu",
"Zach Wood-Doughty",
"Changwei Wang"
] | 2023-09-29 05:29:20 | http://arxiv.org/abs/2309.16992v1 | http://arxiv.org/pdf/2309.16992v1 | 2309.16992v1 |
Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning | Score-based generative models like the diffusion model have been testified to
be effective in modeling multi-modal data from image generation to
reinforcement learning (RL). However, the inference process of diffusion model
can be slow, which hinders its usage in RL with iterative sampling. We propose
to apply the consistency model as an efficient yet expressive policy
representation, namely consistency policy, with an actor-critic style algorithm
for three typical RL settings: offline, offline-to-online and online. For
offline RL, we demonstrate the expressiveness of generative models as policies
from multi-modal data. For offline-to-online RL, the consistency policy is
shown to be more computational efficient than diffusion policy, with a
comparable performance. For online RL, the consistency policy demonstrates
significant speedup and even higher average performances than the diffusion
policy. | [
"Zihan Ding",
"Chi Jin"
] | 2023-09-29 05:05:54 | http://arxiv.org/abs/2309.16984v1 | http://arxiv.org/pdf/2309.16984v1 | 2309.16984v1 |
Reliability Quantification of Deep Reinforcement Learning-based Control | Reliability quantification of deep reinforcement learning (DRL)-based control
is a significant challenge for the practical application of artificial
intelligence (AI) in safety-critical systems. This study proposes a method for
quantifying the reliability of DRL-based control. First, an existing method,
random noise distillation, was applied to the reliability evaluation to clarify
the issues to be solved. Second, a novel method for reliability quantification
was proposed to solve these issues. The reliability is quantified using two
neural networks: reference and evaluator. They have the same structure with the
same initial parameters. The outputs of the two networks were the same before
training. During training, the evaluator network parameters were updated to
maximize the difference between the reference and evaluator networks for
trained data. Thus, the reliability of the DRL-based control for a state can be
evaluated based on the difference in output between the two networks. The
proposed method was applied to DQN-based control as an example of a simple
task, and its effectiveness was demonstrated. Finally, the proposed method was
applied to the problem of switching trained models depending on the state.
Con-sequently, the performance of the DRL-based control was improved by
switching the trained models according to their reliability. | [
"Hitoshi Yoshioka",
"Hirotada Hashimoto"
] | 2023-09-29 04:49:49 | http://arxiv.org/abs/2309.16977v2 | http://arxiv.org/pdf/2309.16977v2 | 2309.16977v2 |
Benchmarking and In-depth Performance Study of Large Language Models on Habana Gaudi Processors | Transformer models have achieved remarkable success in various machine
learning tasks but suffer from high computational complexity and resource
requirements. The quadratic complexity of the self-attention mechanism further
exacerbates these challenges when dealing with long sequences and large
datasets. Specialized AI hardware accelerators, such as the Habana GAUDI
architecture, offer a promising solution to tackle these issues. GAUDI features
a Matrix Multiplication Engine (MME) and a cluster of fully programmable Tensor
Processing Cores (TPC). This paper explores the untapped potential of using
GAUDI processors to accelerate Transformer-based models, addressing key
challenges in the process. Firstly, we provide a comprehensive performance
comparison between the MME and TPC components, illuminating their relative
strengths and weaknesses. Secondly, we explore strategies to optimize MME and
TPC utilization, offering practical insights to enhance computational
efficiency. Thirdly, we evaluate the performance of Transformers on GAUDI,
particularly in handling long sequences and uncovering performance bottlenecks.
Lastly, we evaluate the end-to-end performance of two Transformer-based large
language models (LLM) on GAUDI. The contributions of this work encompass
practical insights for practitioners and researchers alike. We delve into
GAUDI's capabilities for Transformers through systematic profiling, analysis,
and optimization exploration. Our study bridges a research gap and offers a
roadmap for optimizing Transformer-based model training on the GAUDI
architecture. | [
"Chengming Zhang",
"Baixi Sun",
"Xiaodong Yu",
"Zhen Xie",
"Weijian Zheng",
"Kamil Iskra",
"Pete Beckman",
"Dingwen Tao"
] | 2023-09-29 04:49:35 | http://arxiv.org/abs/2309.16976v1 | http://arxiv.org/pdf/2309.16976v1 | 2309.16976v1 |
Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and Smoothness | To obtain a near-optimal policy with fewer interactions in Reinforcement
Learning (RL), a promising approach involves the combination of offline RL,
which enhances sample efficiency by leveraging offline datasets, and online RL,
which explores informative transitions by interacting with the environment.
Offline-to-Online (O2O) RL provides a paradigm for improving an offline trained
agent within limited online interactions. However, due to the significant
distribution shift between online experiences and offline data, most offline RL
algorithms suffer from performance drops and fail to achieve stable policy
improvement in O2O adaptation. To address this problem, we propose the Robust
Offline-to-Online (RO2O) algorithm, designed to enhance offline policies
through uncertainty and smoothness, and to mitigate the performance drop in
online adaptation. Specifically, RO2O incorporates Q-ensemble for uncertainty
penalty and adversarial samples for policy and value smoothness, which enable
RO2O to maintain a consistent learning procedure in online adaptation without
requiring special changes to the learning objective. Theoretical analyses in
linear MDPs demonstrate that the uncertainty and smoothness lead to a tighter
optimality bound in O2O against distribution shift. Experimental results
illustrate the superiority of RO2O in facilitating stable offline-to-online
learning and achieving significant improvement with limited online
interactions. | [
"Xiaoyu Wen",
"Xudong Yu",
"Rui Yang",
"Chenjia Bai",
"Zhen Wang"
] | 2023-09-29 04:42:50 | http://arxiv.org/abs/2309.16973v1 | http://arxiv.org/pdf/2309.16973v1 | 2309.16973v1 |
A Quantum States Preparation Method Based on Difference-Driven Reinforcement Learning | Due to the large state space of the two-qubit system, and the adoption of
ladder reward function in the existing quantum state preparation methods, the
convergence speed is slow and it is difficult to prepare the desired target
quantum state with high fidelity under limited conditions. To solve the above
problems, a difference-driven reinforcement learning (RL) algorithm for quantum
state preparation of two-qubit system is proposed by improving the reward
function and action selection strategy. Firstly, a model is constructed for the
problem of preparing quantum states of a two-qubit system, with restrictions on
the type of quantum gates and the time for quantum state evolution. In the
preparation process, a weighted differential dynamic reward function is
designed to assist the algorithm quickly obtain the maximum expected cumulative
reward. Then, an adaptive e-greedy action selection strategy is adopted to
achieve a balance between exploration and utilization to a certain extent,
thereby improving the fidelity of the final quantum state. The simulation
results show that the proposed algorithm can prepare quantum state with high
fidelity under limited conditions. Compared with other algorithms, it has
different degrees of improvement in convergence speed and fidelity of the final
quantum state. | [
"Wenjie Liu",
"Jing Xu",
"Bosi Wang"
] | 2023-09-29 04:42:11 | http://arxiv.org/abs/2309.16972v1 | http://arxiv.org/pdf/2309.16972v1 | 2309.16972v1 |
Multi-Resolution Active Learning of Fourier Neural Operators | Fourier Neural Operator (FNO) is a popular operator learning framework, which
not only achieves the state-of-the-art performance in many tasks, but also is
highly efficient in training and prediction. However, collecting training data
for the FNO is a costly bottleneck in practice, because it often demands
expensive physical simulations. To overcome this problem, we propose
Multi-Resolution Active learning of FNO (MRA-FNO), which can dynamically select
the input functions and resolutions to lower the data cost as much as possible
while optimizing the learning efficiency. Specifically, we propose a
probabilistic multi-resolution FNO and use ensemble Monte-Carlo to develop an
effective posterior inference algorithm. To conduct active learning, we
maximize a utility-cost ratio as the acquisition function to acquire new
examples and resolutions at each step. We use moment matching and the matrix
determinant lemma to enable tractable, efficient utility computation.
Furthermore, we develop a cost annealing framework to avoid over-penalizing
high-resolution queries at the early stage. The over-penalization is severe
when the cost difference is significant between the resolutions, which renders
active learning often stuck at low-resolution queries and inferior performance.
Our method overcomes this problem and applies to general multi-fidelity active
learning and optimization problems. We have shown the advantage of our method
in several benchmark operator learning tasks. | [
"Shibo Li",
"Xin Yu",
"Wei Xing",
"Mike Kirby",
"Akil Narayan",
"Shandian Zhe"
] | 2023-09-29 04:41:27 | http://arxiv.org/abs/2309.16971v3 | http://arxiv.org/pdf/2309.16971v3 | 2309.16971v3 |
Discrete-Choice Model with Generalized Additive Utility Network | Discrete-choice models are a powerful framework for analyzing decision-making
behavior to provide valuable insights for policymakers and businesses.
Multinomial logit models (MNLs) with linear utility functions have been used in
practice because they are ease to use and interpretable. Recently, MNLs with
neural networks (e.g., ASU-DNN) have been developed, and they have achieved
higher prediction accuracy in behavior choice than classical MNLs. However,
these models lack interpretability owing to complex structures. We developed
utility functions with a novel neural-network architecture based on generalized
additive models, named generalized additive utility network ( GAUNet), for
discrete-choice models. We evaluated the performance of the MNL with GAUNet
using the trip survey data collected in Tokyo. Our models were comparable to
ASU-DNN in accuracy and exhibited improved interpretability compared to
previous models. | [
"Tomoki Nishi",
"Yusuke Hara"
] | 2023-09-29 04:40:01 | http://arxiv.org/abs/2309.16970v1 | http://arxiv.org/pdf/2309.16970v1 | 2309.16970v1 |
Controlling Continuous Relaxation for Combinatorial Optimization | Recent advancements in combinatorial optimization (CO) problems emphasize the
potential of graph neural networks (GNNs). The physics-inspired GNN (PI-GNN)
solver, which finds approximate solutions through unsupervised learning, has
attracted significant attention for large-scale CO problems. Nevertheless,
there has been limited discussion on the performance of the PI-GNN solver for
CO problems on relatively dense graphs where the performance of greedy
algorithms worsens. In addition, since the PI-GNN solver employs a relaxation
strategy, an artificial transformation from the continuous space back to the
original discrete space is necessary after learning, potentially undermining
the robustness of the solutions. This paper numerically demonstrates that the
PI-GNN solver can be trapped in a local solution, where all variables are zero,
in the early stage of learning for CO problems on the dense graphs. Then, we
address these problems by controlling the continuity and discreteness of
relaxed variables while avoiding the local solution: (i) introducing a new
penalty term that controls the continuity and discreteness of the relaxed
variables and eliminates the local solution; (ii) proposing a new continuous
relaxation annealing (CRA) strategy. This new annealing first prioritizes
continuous solutions and intensifies exploration by leveraging the continuity
while avoiding the local solution and then schedules the penalty term for
prioritizing a discrete solution until the relaxed variables are almost
discrete values, which eliminates the need for an artificial transformation
from the continuous to the original discrete space. Empirically, better results
are obtained for CO problems on the dense graphs, where the PI-GNN solver
struggles to find reasonable solutions, and for those on relatively sparse
graphs. Furthermore, the computational time scaling is identical to that of the
PI-GNN solver. | [
"Yuma Ichikawa"
] | 2023-09-29 04:23:58 | http://arxiv.org/abs/2309.16965v1 | http://arxiv.org/pdf/2309.16965v1 | 2309.16965v1 |
Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation | Autonomous vehicle (AV) evaluation has been the subject of increased interest
in recent years both in industry and in academia. This paper focuses on the
development of a novel framework for generating adversarial driving behavior of
background vehicle interfering against the AV to expose effective and rational
risky events. Specifically, the adversarial behavior is learned by a
reinforcement learning (RL) approach incorporated with the cumulative prospect
theory (CPT) which allows representation of human risk cognition. Then, the
extended version of deep deterministic policy gradient (DDPG) technique is
proposed for training the adversarial policy while ensuring training stability
as the CPT action-value function is leveraged. A comparative case study
regarding the cut-in scenario is conducted on a high fidelity
Hardware-in-the-Loop (HiL) platform and the results demonstrate the adversarial
effectiveness to infer the weakness of the tested AV. | [
"Zhen Liu",
"Hang Gao",
"Hao Ma",
"Shuo Cai",
"Yunfeng Hu",
"Ting Qu",
"Hong Chen",
"Xun Gong"
] | 2023-09-29 04:09:46 | http://arxiv.org/abs/2310.00029v2 | http://arxiv.org/pdf/2310.00029v2 | 2310.00029v2 |
Leveraging Optimization for Adaptive Attacks on Image Watermarks | Untrustworthy users can misuse image generators to synthesize high-quality
deepfakes and engage in online spam or disinformation campaigns. Watermarking
deters misuse by marking generated content with a hidden message, enabling its
detection using a secret watermarking key. A core security property of
watermarking is robustness, which states that an attacker can only evade
detection by substantially degrading image quality. Assessing robustness
requires designing an adaptive attack for the specific watermarking algorithm.
A challenge when evaluating watermarking algorithms and their (adaptive)
attacks is to determine whether an adaptive attack is optimal, i.e., it is the
best possible attack. We solve this problem by defining an objective function
and then approach adaptive attacks as an optimization problem. The core idea of
our adaptive attacks is to replicate secret watermarking keys locally by
creating surrogate keys that are differentiable and can be used to optimize the
attack's parameters. We demonstrate for Stable Diffusion models that such an
attacker can break all five surveyed watermarking methods at negligible
degradation in image quality. These findings emphasize the need for more
rigorous robustness testing against adaptive, learnable attackers. | [
"Nils Lukas",
"Abdulrahman Diaa",
"Lucas Fenaux",
"Florian Kerschbaum"
] | 2023-09-29 03:36:42 | http://arxiv.org/abs/2309.16952v1 | http://arxiv.org/pdf/2309.16952v1 | 2309.16952v1 |
Beyond Tides and Time: Machine Learning Triumph in Water Quality | Water resources are essential for sustaining human livelihoods and
environmental well being. Accurate water quality prediction plays a pivotal
role in effective resource management and pollution mitigation. In this study,
we assess the effectiveness of five distinct predictive models linear
regression, Random Forest, XGBoost, LightGBM, and MLP neural network, in
forecasting pH values within the geographical context of Georgia, USA. Notably,
LightGBM emerges as the top performing model, achieving the highest average
precision. Our analysis underscores the supremacy of tree-based models in
addressing regression challenges, while revealing the sensitivity of MLP neural
networks to feature scaling. Intriguingly, our findings shed light on a
counterintuitive discovery: machine learning models, which do not explicitly
account for time dependencies and spatial considerations, outperform spatial
temporal models. This unexpected superiority of machine learning models
challenges conventional assumptions and highlights their potential for
practical applications in water quality prediction. Our research aims to
establish a robust predictive pipeline accessible to both data science experts
and those without domain specific knowledge. In essence, we present a novel
perspective on achieving high prediction accuracy and interpretability in data
science methodologies. Through this study, we redefine the boundaries of water
quality forecasting, emphasizing the significance of data driven approaches
over traditional spatial temporal models. Our findings offer valuable insights
into the evolving landscape of water resource management and environmental
protection. | [
"Yinpu Li",
"Siqi Mao",
"Yaping Yuan",
"Ziren Wang",
"Yixin Kang",
"Yuanxin Yao"
] | 2023-09-29 03:33:53 | http://arxiv.org/abs/2309.16951v2 | http://arxiv.org/pdf/2309.16951v2 | 2309.16951v2 |
Physics-Informed Induction Machine Modelling | This rapid communication devises a Neural Induction Machine (NeuIM) model,
which pilots the use of physics-informed machine learning to enable AI-based
electromagnetic transient simulations. The contributions are threefold: (1) a
formation of NeuIM to represent the induction machine in phase domain; (2) a
physics-informed neural network capable of capturing fast and slow IM dynamics
even in the absence of data; and (3) a data-physics-integrated hybrid NeuIM
approach which is adaptive to various levels of data availability. Extensive
case studies validate the efficacy of NeuIM and in particular, its advantage
over purely data-driven approaches. | [
"Qing Shen",
"Yifan Zhou",
"Peng Zhang"
] | 2023-09-29 02:55:55 | http://arxiv.org/abs/2309.16943v1 | http://arxiv.org/pdf/2309.16943v1 | 2309.16943v1 |
G4SATBench: Benchmarking and Advancing SAT Solving with Graph Neural Networks | Graph neural networks (GNNs) have recently emerged as a promising approach
for solving the Boolean Satisfiability Problem (SAT), offering potential
alternatives to traditional backtracking or local search SAT solvers. However,
despite the growing volume of literature in this field, there remains a notable
absence of a unified dataset and a fair benchmark to evaluate and compare
existing approaches. To address this crucial gap, we present G4SATBench, the
first benchmark study that establishes a comprehensive evaluation framework for
GNN-based SAT solvers. In G4SATBench, we meticulously curate a large and
diverse set of SAT datasets comprising 7 problems with 3 difficulty levels and
benchmark a broad range of GNN models across various prediction tasks, training
objectives, and inference algorithms. To explore the learning abilities and
comprehend the strengths and limitations of GNN-based SAT solvers, we also
compare their solving processes with the heuristics in search-based SAT
solvers. Our empirical results provide valuable insights into the performance
of GNN-based SAT solvers and further suggest that existing GNN models can
effectively learn a solving strategy akin to greedy local search but struggle
to learn backtracking search in the latent space. | [
"Zhaoyu Li",
"Jinpei Guo",
"Xujie Si"
] | 2023-09-29 02:50:57 | http://arxiv.org/abs/2309.16941v1 | http://arxiv.org/pdf/2309.16941v1 | 2309.16941v1 |
PC-Adapter: Topology-Aware Adapter for Efficient Domain Adaption on Point Clouds with Rectified Pseudo-label | Understanding point clouds captured from the real-world is challenging due to
shifts in data distribution caused by varying object scales, sensor angles, and
self-occlusion. Prior works have addressed this issue by combining recent
learning principles such as self-supervised learning, self-training, and
adversarial training, which leads to significant computational overhead.Toward
succinct yet powerful domain adaptation for point clouds, we revisit the unique
challenges of point cloud data under domain shift scenarios and discover the
importance of the global geometry of source data and trends of target
pseudo-labels biased to the source label distribution. Motivated by our
observations, we propose an adapter-guided domain adaptation method,
PC-Adapter, that preserves the global shape information of the source domain
using an attention-based adapter, while learning the local characteristics of
the target domain via another adapter equipped with graph convolution.
Additionally, we propose a novel pseudo-labeling strategy resilient to the
classifier bias by adjusting confidence scores using their class-wise
confidence distributions to consider relative confidences. Our method
demonstrates superiority over baselines on various domain shift settings in
benchmark datasets - PointDA, GraspNetPC, and PointSegDA. | [
"Joonhyung Park",
"Hyunjin Seo",
"Eunho Yang"
] | 2023-09-29 02:32:01 | http://arxiv.org/abs/2309.16936v1 | http://arxiv.org/pdf/2309.16936v1 | 2309.16936v1 |
TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework | Industrial systems demand reliable predictive maintenance strategies to
enhance operational efficiency and reduce downtime. This paper introduces a
novel, integrated framework that leverages the power of transformer neural
networks and deep reinforcement learning (DRL) algorithms to optimize
maintenance actions. Our approach employs the transformer model to effectively
capture complex temporal patterns in sensor data, thereby accurately predicting
the Remaining Useful Life (RUL) of equipment. Simultaneously, the DRL component
of our framework provides cost-effective and timely maintenance
recommendations. We validate the efficacy of our framework on the NASA C-MPASS
dataset, where it demonstrates significant advancements in both RUL prediction
accuracy and the optimization of maintenance actions. Consequently, our
pioneering approach provides an innovative data-driven methodology for
prescriptive maintenance, addressing key challenges in industrial operations
and leading the way to more efficient, cost-effective, and reliable systems. | [
"Yang Zhao",
"Wenbo Wang"
] | 2023-09-29 02:27:54 | http://arxiv.org/abs/2309.16935v1 | http://arxiv.org/pdf/2309.16935v1 | 2309.16935v1 |
Symmetry Leads to Structured Constraint of Learning | Due to common architecture designs, symmetries exist extensively in
contemporary neural networks. In this work, we unveil the importance of the
loss function symmetries in affecting, if not deciding, the learning behavior
of machine learning models. We prove that every mirror symmetry of the loss
function leads to a structured constraint, which becomes a favored solution
when either the weight decay or gradient noise is large. As direct corollaries,
we show that rescaling symmetry leads to sparsity, rotation symmetry leads to
low rankness, and permutation symmetry leads to homogeneous ensembling. Then,
we show that the theoretical framework can explain the loss of plasticity and
various collapse phenomena in neural networks and suggest how symmetries can be
used to design algorithms to enforce hard constraints in a differentiable way. | [
"Liu Ziyin"
] | 2023-09-29 02:21:31 | http://arxiv.org/abs/2309.16932v1 | http://arxiv.org/pdf/2309.16932v1 | 2309.16932v1 |
Learning to Receive Help: Intervention-Aware Concept Embedding Models | Concept Bottleneck Models (CBMs) tackle the opacity of neural architectures
by constructing and explaining their predictions using a set of high-level
concepts. A special property of these models is that they permit concept
interventions, wherein users can correct mispredicted concepts and thus improve
the model's performance. Recent work, however, has shown that intervention
efficacy can be highly dependent on the order in which concepts are intervened
on and on the model's architecture and training hyperparameters. We argue that
this is rooted in a CBM's lack of train-time incentives for the model to be
appropriately receptive to concept interventions. To address this, we propose
Intervention-aware Concept Embedding models (IntCEMs), a novel CBM-based
architecture and training paradigm that improves a model's receptiveness to
test-time interventions. Our model learns a concept intervention policy in an
end-to-end fashion from where it can sample meaningful intervention
trajectories at train-time. This conditions IntCEMs to effectively select and
receive concept interventions when deployed at test-time. Our experiments show
that IntCEMs significantly outperform state-of-the-art concept-interpretable
models when provided with test-time concept interventions, demonstrating the
effectiveness of our approach. | [
"Mateo Espinosa Zarlenga",
"Katherine M. Collins",
"Krishnamurthy Dvijotham",
"Adrian Weller",
"Zohreh Shams",
"Mateja Jamnik"
] | 2023-09-29 02:04:24 | http://arxiv.org/abs/2309.16928v1 | http://arxiv.org/pdf/2309.16928v1 | 2309.16928v1 |
Unlabeled Out-Of-Domain Data Improves Generalization | We propose a novel framework for incorporating unlabeled data into
semi-supervised classification problems, where scenarios involving the
minimization of either i) adversarially robust or ii) non-robust loss functions
have been considered. Notably, we allow the unlabeled samples to deviate
slightly (in total variation sense) from the in-domain distribution. The core
idea behind our framework is to combine Distributionally Robust Optimization
(DRO) with self-supervised training. As a result, we also leverage efficient
polynomial-time algorithms for the training stage. From a theoretical
standpoint, we apply our framework on the classification problem of a mixture
of two Gaussians in $\mathbb{R}^d$, where in addition to the $m$ independent
and labeled samples from the true distribution, a set of $n$ (usually with
$n\gg m$) out of domain and unlabeled samples are gievn as well. Using only the
labeled data, it is known that the generalization error can be bounded by
$\propto\left(d/m\right)^{1/2}$. However, using our method on both isotropic
and non-isotropic Gaussian mixture models, one can derive a new set of
analytically explicit and non-asymptotic bounds which show substantial
improvement on the generalization error compared ERM. Our results underscore
two significant insights: 1) out-of-domain samples, even when unlabeled, can be
harnessed to narrow the generalization gap, provided that the true data
distribution adheres to a form of the "cluster assumption", and 2) the
semi-supervised learning paradigm can be regarded as a special case of our
framework when there are no distributional shifts. We validate our claims
through experiments conducted on a variety of synthetic and real-world
datasets. | [
"Amir Hossein Saberi",
"Amir Najafi",
"Alireza Heidari",
"Mohammad Hosein Movasaghinia",
"Abolfazl Motahari",
"Babak H. Khalaj"
] | 2023-09-29 02:00:03 | http://arxiv.org/abs/2310.00027v1 | http://arxiv.org/pdf/2310.00027v1 | 2310.00027v1 |
Mode Connectivity and Data Heterogeneity of Federated Learning | Federated learning (FL) enables multiple clients to train a model while
keeping their data private collaboratively. Previous studies have shown that
data heterogeneity between clients leads to drifts across client updates.
However, there are few studies on the relationship between client and global
modes, making it unclear where these updates end up drifting. We perform
empirical and theoretical studies on this relationship by utilizing mode
connectivity, which measures performance change (i.e., connectivity) along
parametric paths between different modes. Empirically, reducing data
heterogeneity makes the connectivity on different paths more similar, forming
more low-error overlaps between client and global modes. We also find that a
barrier to connectivity occurs when linearly connecting two global modes, while
it disappears with considering non-linear mode connectivity. Theoretically, we
establish a quantitative bound on the global-mode connectivity using mean-field
theory or dropout stability. The bound demonstrates that the connectivity
improves when reducing data heterogeneity and widening trained models.
Numerical results further corroborate our analytical findings. | [
"Tailin Zhou",
"Jun Zhang",
"Danny H. K. Tsang"
] | 2023-09-29 01:49:03 | http://arxiv.org/abs/2309.16923v1 | http://arxiv.org/pdf/2309.16923v1 | 2309.16923v1 |
ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networks | Graph neural networks (GNNs) have proven their efficacy in a variety of
real-world applications, but their underlying mechanisms remain a mystery. To
address this challenge and enable reliable decision-making, many GNN explainers
have been proposed in recent years. However, these methods often encounter
limitations, including their dependence on specific instances, lack of
generalizability to unseen graphs, producing potentially invalid explanations,
and yielding inadequate fidelity. To overcome these limitations, we, in this
paper, introduce the Auxiliary Classifier Generative Adversarial Network
(ACGAN) into the field of GNN explanation and propose a new GNN explainer
dubbed~\emph{ACGAN-GNNExplainer}. Our approach leverages a generator to produce
explanations for the original input graphs while incorporating a discriminator
to oversee the generation process, ensuring explanation fidelity and improving
accuracy. Experimental evaluations conducted on both synthetic and real-world
graph datasets demonstrate the superiority of our proposed method compared to
other existing GNN explainers. | [
"Yiqiao Li",
"Jianlong Zhou",
"Yifei Dong",
"Niusha Shafiabady",
"Fang Chen"
] | 2023-09-29 01:20:28 | http://arxiv.org/abs/2309.16918v2 | http://arxiv.org/pdf/2309.16918v2 | 2309.16918v2 |
ONNXExplainer: an ONNX Based Generic Framework to Explain Neural Networks Using Shapley Values | Understanding why a neural network model makes certain decisions can be as
important as the inference performance. Various methods have been proposed to
help practitioners explain the prediction of a neural network model, of which
Shapley values are most popular. SHAP package is a leading implementation of
Shapley values to explain neural networks implemented in TensorFlow or PyTorch
but lacks cross-platform support, one-shot deployment and is highly
inefficient. To address these problems, we present the ONNXExplainer, which is
a generic framework to explain neural networks using Shapley values in the ONNX
ecosystem. In ONNXExplainer, we develop its own automatic differentiation and
optimization approach, which not only enables One-Shot Deployment of neural
networks inference and explanations, but also significantly improves the
efficiency to compute explanation with less memory consumption. For fair
comparison purposes, we also implement the same optimization in TensorFlow and
PyTorch and measure its performance against the current state of the art
open-source counterpart, SHAP. Extensive benchmarks demonstrate that the
proposed optimization approach improves the explanation latency of VGG19,
ResNet50, DenseNet201, and EfficientNetB0 by as much as 500%. | [
"Yong Zhao",
"Runxin He",
"Nicholas Kersting",
"Can Liu",
"Shubham Agrawal",
"Chiranjeet Chetia",
"Yu Gu"
] | 2023-09-29 01:07:38 | http://arxiv.org/abs/2309.16916v2 | http://arxiv.org/pdf/2309.16916v2 | 2309.16916v2 |
Algorithmic Recourse for Anomaly Detection in Multivariate Time Series | Anomaly detection in multivariate time series has received extensive study
due to the wide spectrum of applications. An anomaly in multivariate time
series usually indicates a critical event, such as a system fault or an
external attack. Therefore, besides being effective in anomaly detection,
recommending anomaly mitigation actions is also important in practice yet
under-investigated. In this work, we focus on algorithmic recourse in time
series anomaly detection, which is to recommend fixing actions on abnormal time
series with a minimum cost so that domain experts can understand how to fix the
abnormal behavior. To this end, we propose an algorithmic recourse framework,
called RecAD, which can recommend recourse actions to flip the abnormal time
steps. Experiments on two synthetic and one real-world datasets show the
effectiveness of our framework. | [
"Xiao Han",
"Lu Zhang",
"Yongkai Wu",
"Shuhan Yuan"
] | 2023-09-28 23:50:11 | http://arxiv.org/abs/2309.16896v1 | http://arxiv.org/pdf/2309.16896v1 | 2309.16896v1 |
Sourcing Investment Targets for Venture and Growth Capital Using Multivariate Time Series Transformer | This paper addresses the growing application of data-driven approaches within
the Private Equity (PE) industry, particularly in sourcing investment targets
(i.e., companies) for Venture Capital (VC) and Growth Capital (GC). We present
a comprehensive review of the relevant approaches and propose a novel approach
leveraging a Transformer-based Multivariate Time Series Classifier (TMTSC) for
predicting the success likelihood of any candidate company. The objective of
our research is to optimize sourcing performance for VC and GC investments by
formally defining the sourcing problem as a multivariate time series
classification task. We consecutively introduce the key components of our
implementation which collectively contribute to the successful application of
TMTSC in VC/GC sourcing: input features, model architecture, optimization
target, and investor-centric data augmentation and split. Our extensive
experiments on four datasets, benchmarked towards three popular baselines,
demonstrate the effectiveness of our approach in improving decision making
within the VC and GC industry. | [
"Lele Cao",
"Gustaf Halvardsson",
"Andrew McCornack",
"Vilhelm von Ehrenheim",
"Pawel Herman"
] | 2023-09-28 23:03:12 | http://arxiv.org/abs/2309.16888v1 | http://arxiv.org/pdf/2309.16888v1 | 2309.16888v1 |
The Lipschitz-Variance-Margin Tradeoff for Enhanced Randomized Smoothing | Real-life applications of deep neural networks are hindered by their unsteady
predictions when faced with noisy inputs and adversarial attacks. The certified
radius is in this context a crucial indicator of the robustness of models.
However how to design an efficient classifier with a sufficient certified
radius? Randomized smoothing provides a promising framework by relying on noise
injection in inputs to obtain a smoothed and more robust classifier. In this
paper, we first show that the variance introduced by randomized smoothing
closely interacts with two other important properties of the classifier, i.e.
its Lipschitz constant and margin. More precisely, our work emphasizes the dual
impact of the Lipschitz constant of the base classifier, on both the smoothed
classifier and the empirical variance. Moreover, to increase the certified
robust radius, we introduce a different simplex projection technique for the
base classifier to leverage the variance-margin trade-off thanks to Bernstein's
concentration inequality, along with an enhanced Lipschitz bound. Experimental
results show a significant improvement in certified accuracy compared to
current state-of-the-art methods. Our novel certification procedure allows us
to use pre-trained models that are used with randomized smoothing, effectively
improving the current certification radius in a zero-shot manner. | [
"Blaise Delattre",
"Alexandre Araujo",
"Quentin Barthélemy",
"Alexandre Allauzen"
] | 2023-09-28 22:41:47 | http://arxiv.org/abs/2309.16883v1 | http://arxiv.org/pdf/2309.16883v1 | 2309.16883v1 |
Message Propagation Through Time: An Algorithm for Sequence Dependency Retention in Time Series Modeling | Time series modeling, a crucial area in science, often encounters challenges
when training Machine Learning (ML) models like Recurrent Neural Networks
(RNNs) using the conventional mini-batch training strategy that assumes
independent and identically distributed (IID) samples and initializes RNNs with
zero hidden states. The IID assumption ignores temporal dependencies among
samples, resulting in poor performance. This paper proposes the Message
Propagation Through Time (MPTT) algorithm to effectively incorporate long
temporal dependencies while preserving faster training times relative to the
stateful solutions. MPTT utilizes two memory modules to asynchronously manage
initial hidden states for RNNs, fostering seamless information exchange between
samples and allowing diverse mini-batches throughout epochs. MPTT further
implements three policies to filter outdated and preserve essential information
in the hidden states to generate informative initial hidden states for RNNs,
facilitating robust training. Experimental results demonstrate that MPTT
outperforms seven strategies on four climate datasets with varying levels of
temporal dependencies. | [
"Shaoming Xu",
"Ankush Khandelwal",
"Arvind Renganathan",
"Vipin Kumar"
] | 2023-09-28 22:38:18 | http://arxiv.org/abs/2309.16882v1 | http://arxiv.org/pdf/2309.16882v1 | 2309.16882v1 |
Investigating Human-Identifiable Features Hidden in Adversarial Perturbations | Neural networks perform exceedingly well across various machine learning
tasks but are not immune to adversarial perturbations. This vulnerability has
implications for real-world applications. While much research has been
conducted, the underlying reasons why neural networks fall prey to adversarial
attacks are not yet fully understood. Central to our study, which explores up
to five attack algorithms across three datasets, is the identification of
human-identifiable features in adversarial perturbations. Additionally, we
uncover two distinct effects manifesting within human-identifiable features.
Specifically, the masking effect is prominent in untargeted attacks, while the
generation effect is more common in targeted attacks. Using pixel-level
annotations, we extract such features and demonstrate their ability to
compromise target models. In addition, our findings indicate a notable extent
of similarity in perturbations across different attack algorithms when averaged
over multiple models. This work also provides insights into phenomena
associated with adversarial perturbations, such as transferability and model
interpretability. Our study contributes to a deeper understanding of the
underlying mechanisms behind adversarial attacks and offers insights for the
development of more resilient defense strategies for neural networks. | [
"Dennis Y. Menn",
"Tzu-hsun Feng",
"Sriram Vishwanath",
"Hung-yi Lee"
] | 2023-09-28 22:31:29 | http://arxiv.org/abs/2309.16878v1 | http://arxiv.org/pdf/2309.16878v1 | 2309.16878v1 |
LEF: Late-to-Early Temporal Fusion for LiDAR 3D Object Detection | We propose a late-to-early recurrent feature fusion scheme for 3D object
detection using temporal LiDAR point clouds. Our main motivation is fusing
object-aware latent embeddings into the early stages of a 3D object detector.
This feature fusion strategy enables the model to better capture the shapes and
poses for challenging objects, compared with learning from raw points directly.
Our method conducts late-to-early feature fusion in a recurrent manner. This is
achieved by enforcing window-based attention blocks upon temporally calibrated
and aligned sparse pillar tokens. Leveraging bird's eye view foreground pillar
segmentation, we reduce the number of sparse history features that our model
needs to fuse into its current frame by 10$\times$. We also propose a
stochastic-length FrameDrop training technique, which generalizes the model to
variable frame lengths at inference for improved performance without
retraining. We evaluate our method on the widely adopted Waymo Open Dataset and
demonstrate improvement on 3D object detection against the baseline model,
especially for the challenging category of large objects. | [
"Tong He",
"Pei Sun",
"Zhaoqi Leng",
"Chenxi Liu",
"Dragomir Anguelov",
"Mingxing Tan"
] | 2023-09-28 21:58:25 | http://arxiv.org/abs/2309.16870v1 | http://arxiv.org/pdf/2309.16870v1 | 2309.16870v1 |
Sharp Generalization of Transductive Learning: A Transductive Local Rademacher Complexity Approach | We introduce a new tool, Transductive Local Rademacher Complexity (TLRC), to
analyze the generalization performance of transductive learning methods and
motivate new transductive learning algorithms. Our work extends the idea of the
popular Local Rademacher Complexity (LRC) to the transductive setting with
considerable changes compared to the analysis of typical LRC methods in the
inductive setting. We present a localized version of Rademacher complexity
based tool wihch can be applied to various transductive learning problems and
gain sharp bounds under proper conditions. Similar to the development of LRC,
we build TLRC by starting from a sharp concentration inequality for independent
variables with variance information. The prediction function class of a
transductive learning model is then divided into pieces with a sub-root
function being the upper bound for the Rademacher complexity of each piece, and
the variance of all the functions in each piece is limited. A carefully
designed variance operator is used to ensure that the bound for the test loss
on unlabeled test data in the transductive setting enjoys a remarkable
similarity to that of the classical LRC bound in the inductive setting. We use
the new TLRC tool to analyze the Transductive Kernel Learning (TKL) model,
where the labels of test data are generated by a kernel function. The result of
TKL lays the foundation for generalization bounds for two types of transductive
learning tasks, Graph Transductive Learning (GTL) and Transductive
Nonparametric Kernel Regression (TNKR). When the target function is
low-dimensional or approximately low-dimensional, we design low rank methods
for both GTL and TNKR, which enjoy particularly sharper generalization bounds
by TLRC which cannot be achieved by existing learning theory methods, to the
best of our knowledge. | [
"Yingzhen Yang"
] | 2023-09-28 21:21:44 | http://arxiv.org/abs/2309.16858v1 | http://arxiv.org/pdf/2309.16858v1 | 2309.16858v1 |
Preface: A Data-driven Volumetric Prior for Few-shot Ultra High-resolution Face Synthesis | NeRFs have enabled highly realistic synthesis of human faces including
complex appearance and reflectance effects of hair and skin. These methods
typically require a large number of multi-view input images, making the process
hardware intensive and cumbersome, limiting applicability to unconstrained
settings. We propose a novel volumetric human face prior that enables the
synthesis of ultra high-resolution novel views of subjects that are not part of
the prior's training distribution. This prior model consists of an
identity-conditioned NeRF, trained on a dataset of low-resolution multi-view
images of diverse humans with known camera calibration. A simple sparse
landmark-based 3D alignment of the training dataset allows our model to learn a
smooth latent space of geometry and appearance despite a limited number of
training identities. A high-quality volumetric representation of a novel
subject can be obtained by model fitting to 2 or 3 camera views of arbitrary
resolution. Importantly, our method requires as few as two views of casually
captured images as input at inference time. | [
"Marcel C. Bühler",
"Kripasindhu Sarkar",
"Tanmay Shah",
"Gengyan Li",
"Daoye Wang",
"Leonhard Helminger",
"Sergio Orts-Escolano",
"Dmitry Lagun",
"Otmar Hilliges",
"Thabo Beeler",
"Abhimitra Meka"
] | 2023-09-28 21:21:44 | http://arxiv.org/abs/2309.16859v1 | http://arxiv.org/pdf/2309.16859v1 | 2309.16859v1 |
Applications of Federated Learning in IoT for Hyper Personalisation | Billions of IoT devices are being deployed, taking advantage of faster
internet, and the opportunity to access more endpoints. Vast quantities of data
are being generated constantly by these devices but are not effectively being
utilised. Using FL training machine learning models over these multiple clients
without having to bring it to a central server. We explore how to use such a
model to implement ultra levels of personalization unlike before | [
"Veer Dosi"
] | 2023-09-28 21:07:40 | http://arxiv.org/abs/2309.16854v1 | http://arxiv.org/pdf/2309.16854v1 | 2309.16854v1 |
Space-Time Attention with Shifted Non-Local Search | Efficiently computing attention maps for videos is challenging due to the
motion of objects between frames. While a standard non-local search is
high-quality for a window surrounding each query point, the window's small size
cannot accommodate motion. Methods for long-range motion use an auxiliary
network to predict the most similar key coordinates as offsets from each query
location. However, accurately predicting this flow field of offsets remains
challenging, even for large-scale networks. Small spatial inaccuracies
significantly impact the attention module's quality. This paper proposes a
search strategy that combines the quality of a non-local search with the range
of predicted offsets. The method, named Shifted Non-Local Search, executes a
small grid search surrounding the predicted offsets to correct small spatial
errors. Our method's in-place computation consumes 10 times less memory and is
over 3 times faster than previous work. Experimentally, correcting the small
spatial errors improves the video frame alignment quality by over 3 dB PSNR.
Our search upgrades existing space-time attention modules, which improves video
denoising results by 0.30 dB PSNR for a 7.5% increase in overall runtime. We
integrate our space-time attention module into a UNet-like architecture to
achieve state-of-the-art results on video denoising. | [
"Kent Gauen",
"Stanley Chan"
] | 2023-09-28 20:59:51 | http://arxiv.org/abs/2309.16849v1 | http://arxiv.org/pdf/2309.16849v1 | 2309.16849v1 |
Optimal Nonlinearities Improve Generalization Performance of Random Features | Random feature model with a nonlinear activation function has been shown to
perform asymptotically equivalent to a Gaussian model in terms of training and
generalization errors. Analysis of the equivalent model reveals an important
yet not fully understood role played by the activation function. To address
this issue, we study the "parameters" of the equivalent model to achieve
improved generalization performance for a given supervised learning problem. We
show that acquired parameters from the Gaussian model enable us to define a set
of optimal nonlinearities. We provide two example classes from this set, e.g.,
second-order polynomial and piecewise linear functions. These functions are
optimized to improve generalization performance regardless of the actual form.
We experiment with regression and classification problems, including synthetic
and real (e.g., CIFAR10) data. Our numerical results validate that the
optimized nonlinearities achieve better generalization performance than
widely-used nonlinear functions such as ReLU. Furthermore, we illustrate that
the proposed nonlinearities also mitigate the so-called double descent
phenomenon, which is known as the non-monotonic generalization performance
regarding the sample size and the model size. | [
"Samet Demir",
"Zafer Doğan"
] | 2023-09-28 20:55:21 | http://arxiv.org/abs/2309.16846v1 | http://arxiv.org/pdf/2309.16846v1 | 2309.16846v1 |
Constant Approximation for Individual Preference Stable Clustering | Individual preference (IP) stability, introduced by Ahmadi et al. (ICML
2022), is a natural clustering objective inspired by stability and fairness
constraints. A clustering is $\alpha$-IP stable if the average distance of
every data point to its own cluster is at most $\alpha$ times the average
distance to any other cluster. Unfortunately, determining if a dataset admits a
$1$-IP stable clustering is NP-Hard. Moreover, before this work, it was unknown
if an $o(n)$-IP stable clustering always \emph{exists}, as the prior state of
the art only guaranteed an $O(n)$-IP stable clustering. We close this gap in
understanding and show that an $O(1)$-IP stable clustering always exists for
general metrics, and we give an efficient algorithm which outputs such a
clustering. We also introduce generalizations of IP stability beyond average
distance and give efficient, near-optimal algorithms in the cases where we
consider the maximum and minimum distances within and between clusters. | [
"Anders Aamand",
"Justin Y. Chen",
"Allen Liu",
"Sandeep Silwal",
"Pattara Sukprasert",
"Ali Vakilian",
"Fred Zhang"
] | 2023-09-28 20:42:46 | http://arxiv.org/abs/2309.16840v1 | http://arxiv.org/pdf/2309.16840v1 | 2309.16840v1 |
Propagation and Attribution of Uncertainty in Medical Imaging Pipelines | Uncertainty estimation, which provides a means of building explainable neural
networks for medical imaging applications, have mostly been studied for single
deep learning models that focus on a specific task. In this paper, we propose a
method to propagate uncertainty through cascades of deep learning models in
medical imaging pipelines. This allows us to aggregate the uncertainty in later
stages of the pipeline and to obtain a joint uncertainty measure for the
predictions of later models. Additionally, we can separately report
contributions of the aleatoric, data-based, uncertainty of every component in
the pipeline. We demonstrate the utility of our method on a realistic imaging
pipeline that reconstructs undersampled brain and knee magnetic resonance (MR)
images and subsequently predicts quantitative information from the images, such
as the brain volume, or knee side or patient's sex. We quantitatively show that
the propagated uncertainty is correlated with input uncertainty and compare the
proportions of contributions of pipeline stages to the joint uncertainty
measure. | [
"Leonhard F. Feiner",
"Martin J. Menten",
"Kerstin Hammernik",
"Paul Hager",
"Wenqi Huang",
"Daniel Rueckert",
"Rickmer F. Braren",
"Georgios Kaissis"
] | 2023-09-28 20:23:25 | http://arxiv.org/abs/2309.16831v1 | http://arxiv.org/pdf/2309.16831v1 | 2309.16831v1 |
An analysis of the derivative-free loss method for solving PDEs | This study analyzes the derivative-free loss method to solve a certain class
of elliptic PDEs using neural networks. The derivative-free loss method uses
the Feynman-Kac formulation, incorporating stochastic walkers and their
corresponding average values. We investigate the effect of the time interval
related to the Feynman-Kac formulation and the walker size in the context of
computational efficiency, trainability, and sampling errors. Our analysis shows
that the training loss bias is proportional to the time interval and the
spatial gradient of the neural network while inversely proportional to the
walker size. We also show that the time interval must be sufficiently long to
train the network. These analytic results tell that we can choose the walker
size as small as possible based on the optimal lower bound of the time
interval. We also provide numerical tests supporting our analysis. | [
"Jihun Han",
"Yoonsang Lee"
] | 2023-09-28 20:19:51 | http://arxiv.org/abs/2309.16829v1 | http://arxiv.org/pdf/2309.16829v1 | 2309.16829v1 |
Post-Training Overfitting Mitigation in DNN Classifiers | Well-known (non-malicious) sources of overfitting in deep neural net (DNN)
classifiers include: i) large class imbalances; ii) insufficient training-set
diversity; and iii) over-training. In recent work, it was shown that backdoor
data-poisoning also induces overfitting, with unusually large classification
margins to the attacker's target class, mediated particularly by (unbounded)
ReLU activations that allow large signals to propagate in the DNN. Thus, an
effective post-training (with no knowledge of the training set or training
process) mitigation approach against backdoors was proposed, leveraging a small
clean dataset, based on bounding neural activations. Improving upon that work,
we threshold activations specifically to limit maximum margins (MMs), which
yields performance gains in backdoor mitigation. We also provide some
analytical support for this mitigation approach. Most importantly, we show that
post-training MM-based regularization substantially mitigates non-malicious
overfitting due to class imbalances and overtraining. Thus, unlike adversarial
training, which provides some resilience against attacks but which harms clean
(attack-free) generalization, we demonstrate an approach originating from
adversarial learning that helps clean generalization accuracy. Experiments on
CIFAR-10 and CIFAR-100, in comparison with peer methods, demonstrate strong
performance of our methods. | [
"Hang Wang",
"David J. Miller",
"George Kesidis"
] | 2023-09-28 20:16:24 | http://arxiv.org/abs/2309.16827v1 | http://arxiv.org/pdf/2309.16827v1 | 2309.16827v1 |
FENDA-FL: Personalized Federated Learning on Heterogeneous Clinical Datasets | Federated learning (FL) is increasingly being recognized as a key approach to
overcoming the data silos that so frequently obstruct the training and
deployment of machine-learning models in clinical settings. This work
contributes to a growing body of FL research specifically focused on clinical
applications along three important directions. First, an extension of the FENDA
method (Kim et al., 2016) to the FL setting is proposed. Experiments conducted
on the FLamby benchmarks (du Terrail et al., 2022a) and GEMINI datasets (Verma
et al., 2017) show that the approach is robust to heterogeneous clinical data
and often outperforms existing global and personalized FL techniques. Further,
the experimental results represent substantive improvements over the original
FLamby benchmarks and expand such benchmarks to include evaluation of
personalized FL methods. Finally, we advocate for a comprehensive checkpointing
and evaluation framework for FL to better reflect practical settings and
provide multiple baselines for comparison. | [
"Fatemeh Tavakoli",
"D. B. Emerson",
"John Jewell",
"Amrit Krishnan",
"Yuchong Zhang",
"Amol Verma",
"Fahad Razak"
] | 2023-09-28 20:12:17 | http://arxiv.org/abs/2309.16825v1 | http://arxiv.org/pdf/2309.16825v1 | 2309.16825v1 |
Multi-Bellman operator for convergence of $Q$-learning with linear function approximation | We study the convergence of $Q$-learning with linear function approximation.
Our key contribution is the introduction of a novel multi-Bellman operator that
extends the traditional Bellman operator. By exploring the properties of this
operator, we identify conditions under which the projected multi-Bellman
operator becomes contractive, providing improved fixed-point guarantees
compared to the Bellman operator. To leverage these insights, we propose the
multi $Q$-learning algorithm with linear function approximation. We demonstrate
that this algorithm converges to the fixed-point of the projected multi-Bellman
operator, yielding solutions of arbitrary accuracy. Finally, we validate our
approach by applying it to well-known environments, showcasing the
effectiveness and applicability of our findings. | [
"Diogo S. Carvalho",
"Pedro A. Santos",
"Francisco S. Melo"
] | 2023-09-28 19:56:31 | http://arxiv.org/abs/2309.16819v1 | http://arxiv.org/pdf/2309.16819v1 | 2309.16819v1 |
PROSE: Predicting Operators and Symbolic Expressions using Multimodal Transformers | Approximating nonlinear differential equations using a neural network
provides a robust and efficient tool for various scientific computing tasks,
including real-time predictions, inverse problems, optimal controls, and
surrogate modeling. Previous works have focused on embedding dynamical systems
into networks through two approaches: learning a single solution operator
(i.e., the mapping from input parametrized functions to solutions) or learning
the governing system of equations (i.e., the constitutive model relative to the
state variables). Both of these approaches yield different representations for
the same underlying data or function. Additionally, observing that families of
differential equations often share key characteristics, we seek one network
representation across a wide range of equations. Our method, called Predicting
Operators and Symbolic Expressions (PROSE), learns maps from multimodal inputs
to multimodal outputs, capable of generating both numerical predictions and
mathematical equations. By using a transformer structure and a feature fusion
approach, our network can simultaneously embed sets of solution operators for
various parametric differential equations using a single trained network.
Detailed experiments demonstrate that the network benefits from its multimodal
nature, resulting in improved prediction accuracy and better generalization.
The network is shown to be able to handle noise in the data and errors in the
symbolic representation, including noisy numerical values, model
misspecification, and erroneous addition or deletion of terms. PROSE provides a
new neural network framework for differential equations which allows for more
flexibility and generality in learning operators and governing equations from
data. | [
"Yuxuan Liu",
"Zecheng Zhang",
"Hayden Schaeffer"
] | 2023-09-28 19:46:07 | http://arxiv.org/abs/2309.16816v1 | http://arxiv.org/pdf/2309.16816v1 | 2309.16816v1 |
GraB-sampler: Optimal Permutation-based SGD Data Sampler for PyTorch | The online Gradient Balancing (GraB) algorithm greedily choosing the examples
ordering by solving the herding problem using per-sample gradients is proved to
be the theoretically optimal solution that guarantees to outperform Random
Reshuffling. However, there is currently no efficient implementation of GraB
for the community to easily use it.
This work presents an efficient Python library, $\textit{GraB-sampler}$, that
allows the community to easily use GraB algorithms and proposes 5 variants of
the GraB algorithm. The best performance result of the GraB-sampler reproduces
the training loss and test accuracy results while only in the cost of 8.7%
training time overhead and 0.85% peak GPU memory usage overhead. | [
"Guanghao Wei"
] | 2023-09-28 19:31:36 | http://arxiv.org/abs/2309.16809v1 | http://arxiv.org/pdf/2309.16809v1 | 2309.16809v1 |
Granularity at Scale: Estimating Neighborhood Well-Being from High-Resolution Orthographic Imagery and Hybrid Learning | Many areas of the world are without basic information on the well-being of
the residing population due to limitations in existing data collection methods.
Overhead images obtained remotely, such as from satellite or aircraft, can help
serve as windows into the state of life on the ground and help "fill in the
gaps" where community information is sparse, with estimates at smaller
geographic scales requiring higher resolution sensors. Concurrent with improved
sensor resolutions, recent advancements in machine learning and computer vision
have made it possible to quickly extract features from and detect patterns in
image data, in the process correlating these features with other information.
In this work, we explore how well two approaches, a supervised convolutional
neural network and semi-supervised clustering based on bag-of-visual-words,
estimate population density, median household income, and educational
attainment of individual neighborhoods from publicly available high-resolution
imagery of cities throughout the United States. Results and analyses indicate
that features extracted from the imagery can accurately estimate the density
(R$^2$ up to 0.81) of neighborhoods, with the supervised approach able to
explain about half the variation in a population's income and education. In
addition to the presented approaches serving as a basis for further geographic
generalization, the novel semi-supervised approach provides a foundation for
future work seeking to estimate fine-scale information from overhead imagery
without the need for label data. | [
"Ethan Brewer",
"Giovani Valdrighi",
"Parikshit Solunke",
"Joao Rulff",
"Yurii Piadyk",
"Zhonghui Lv",
"Jorge Poco",
"Claudio Silva"
] | 2023-09-28 19:30:26 | http://arxiv.org/abs/2309.16808v1 | http://arxiv.org/pdf/2309.16808v1 | 2309.16808v1 |
De-SaTE: Denoising Self-attention Transformer Encoders for Li-ion Battery Health Prognostics | Lithium Ion (Li-ion) batteries have gained widespread popularity across
various industries, from powering portable electronic devices to propelling
electric vehicles and supporting energy storage systems. A central challenge in
managing Li-ion batteries effectively is accurately predicting their Remaining
Useful Life (RUL), which is a critical measure for proactive maintenance and
predictive analytics. This study presents a novel approach that harnesses the
power of multiple denoising modules, each trained to address specific types of
noise commonly encountered in battery data. Specifically we use a denoising
auto-encoder and a wavelet denoiser to generate encoded/decomposed
representations, which are subsequently processed through dedicated
self-attention transformer encoders. After extensive experimentation on the
NASA and CALCE datasets, we are able to characterize a broad spectrum of health
indicator estimations under a set of diverse noise patterns. We find that our
reported error metrics on these datasets are on par or better with the best
reported in recent literature. | [
"Gaurav Shinde",
"Rohan Mohapatra",
"Pooja Krishan",
"Saptarshi Sengupta"
] | 2023-09-28 19:17:13 | http://arxiv.org/abs/2310.00023v1 | http://arxiv.org/pdf/2310.00023v1 | 2310.00023v1 |
Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution | Popular prompt strategies like Chain-of-Thought Prompting can dramatically
improve the reasoning abilities of Large Language Models (LLMs) in various
domains. However, such hand-crafted prompt-strategies are often sub-optimal. In
this paper, we present Promptbreeder, a general-purpose self-referential
self-improvement mechanism that evolves and adapts prompts for a given domain.
Driven by an LLM, Promptbreeder mutates a population of task-prompts, and
subsequently evaluates them for fitness on a training set. Crucially, the
mutation of these task-prompts is governed by mutation-prompts that the LLM
generates and improves throughout evolution in a self-referential way. That is,
Promptbreeder is not just improving task-prompts, but it is also improving the
mutationprompts that improve these task-prompts. Promptbreeder outperforms
state-of-the-art prompt strategies such as Chain-of-Thought and Plan-and-Solve
Prompting on commonly used arithmetic and commonsense reasoning benchmarks.
Furthermore, Promptbreeder is able to evolve intricate task-prompts for the
challenging problem of hate speech classification. | [
"Chrisantha Fernando",
"Dylan Banarse",
"Henryk Michalewski",
"Simon Osindero",
"Tim Rocktäschel"
] | 2023-09-28 19:01:07 | http://arxiv.org/abs/2309.16797v1 | http://arxiv.org/pdf/2309.16797v1 | 2309.16797v1 |
Hallucination Reduction in Long Input Text Summarization | Hallucination in text summarization refers to the phenomenon where the model
generates information that is not supported by the input source document.
Hallucination poses significant obstacles to the accuracy and reliability of
the generated summaries. In this paper, we aim to reduce hallucinated outputs
or hallucinations in summaries of long-form text documents. We have used the
PubMed dataset, which contains long scientific research documents and their
abstracts. We have incorporated the techniques of data filtering and joint
entity and summary generation (JAENS) in the fine-tuning of the Longformer
Encoder-Decoder (LED) model to minimize hallucinations and thereby improve the
quality of the generated summary. We have used the following metrics to measure
factual consistency at the entity level: precision-source, and F1-target. Our
experiments show that the fine-tuned LED model performs well in generating the
paper abstract. Data filtering techniques based on some preprocessing steps
reduce entity-level hallucinations in the generated summaries in terms of some
of the factual consistency metrics. | [
"Tohida Rehman",
"Ronit Mandal",
"Abhishek Agarwal",
"Debarshi Kumar Sanyal"
] | 2023-09-28 18:22:16 | http://arxiv.org/abs/2309.16781v1 | http://arxiv.org/pdf/2309.16781v1 | 2309.16781v1 |
Intriguing properties of generative classifiers | What is the best paradigm to recognize objects -- discriminative inference
(fast but potentially prone to shortcut learning) or using a generative model
(slow but potentially more robust)? We build on recent advances in generative
modeling that turn text-to-image models into classifiers. This allows us to
study their behavior and to compare them against discriminative models and
human psychophysical data. We report four intriguing emergent properties of
generative classifiers: they show a record-breaking human-like shape bias (99%
for Imagen), near human-level out-of-distribution accuracy, state-of-the-art
alignment with human classification errors, and they understand certain
perceptual illusions. Our results indicate that while the current dominant
paradigm for modeling human object recognition is discriminative inference,
zero-shot generative models approximate human object recognition data
surprisingly well. | [
"Priyank Jaini",
"Kevin Clark",
"Robert Geirhos"
] | 2023-09-28 18:19:40 | http://arxiv.org/abs/2309.16779v1 | http://arxiv.org/pdf/2309.16779v1 | 2309.16779v1 |
Neural scaling laws for phenotypic drug discovery | Recent breakthroughs by deep neural networks (DNNs) in natural language
processing (NLP) and computer vision have been driven by a scale-up of models
and data rather than the discovery of novel computing paradigms. Here, we
investigate if scale can have a similar impact for models designed to aid small
molecule drug discovery. We address this question through a large-scale and
systematic analysis of how DNN size, data diet, and learning routines interact
to impact accuracy on our Phenotypic Chemistry Arena (Pheno-CA) benchmark: a
diverse set of drug development tasks posed on image-based high content
screening data. Surprisingly, we find that DNNs explicitly supervised to solve
tasks in the Pheno-CA do not continuously improve as their data and model size
is scaled-up. To address this issue, we introduce a novel precursor task, the
Inverse Biological Process (IBP), which is designed to resemble the causal
objective functions that have proven successful for NLP. We indeed find that
DNNs first trained with IBP then probed for performance on the Pheno-CA
significantly outperform task-supervised DNNs. More importantly, the
performance of these IBP-trained DNNs monotonically improves with data and
model scale. Our findings reveal that the DNN ingredients needed to accurately
solve small molecule drug development tasks are already in our hands, and
project how much more experimental data is needed to achieve any desired level
of improvement. We release our Pheno-CA benchmark and code to encourage further
study of neural scaling laws for small molecule drug discovery. | [
"Drew Linsley",
"John Griffin",
"Jason Parker Brown",
"Adam N Roose",
"Michael Frank",
"Peter Linsley",
"Steven Finkbeiner",
"Jeremy Linsley"
] | 2023-09-28 18:10:43 | http://arxiv.org/abs/2309.16773v1 | http://arxiv.org/pdf/2309.16773v1 | 2309.16773v1 |
Persona-Coded Poly-Encoder: Persona-Guided Multi-Stream Conversational Sentence Scoring | Recent advances in machine learning and deep learning have led to the
widespread use of Conversational AI in many practical applications. However, it
is still very challenging to leverage auxiliary information that can provide
conversational context or personalized tuning to improve the quality of
conversations. For example, there has only been limited research on using an
individuals persona information to improve conversation quality, and even
state-of-the-art conversational AI techniques are unable to effectively
leverage signals from heterogeneous sources of auxiliary data, such as
multi-modal interaction data, demographics, SDOH data, etc. In this paper, we
present a novel Persona-Coded Poly-Encoder method that leverages persona
information in a multi-stream encoding scheme to improve the quality of
response generation for conversations. To show the efficacy of the proposed
method, we evaluate our method on two different persona-based conversational
datasets, and compared against two state-of-the-art methods. Our experimental
results and analysis demonstrate that our method can improve conversation
quality over the baseline method Poly-Encoder by 3.32% and 2.94% in terms of
BLEU score and HR@1, respectively. More significantly, our method offers a path
to better utilization of multi-modal data in conversational tasks. Lastly, our
study outlines several challenges and future research directions for advancing
personalized conversational AI technology. | [
"Junfeng Liu",
"Christopher Symons",
"Ranga Raju Vatsavai"
] | 2023-09-28 18:07:01 | http://arxiv.org/abs/2309.16770v1 | http://arxiv.org/pdf/2309.16770v1 | 2309.16770v1 |
Learning to Transform for Generalizable Instance-wise Invariance | Computer vision research has long aimed to build systems that are robust to
spatial transformations found in natural data. Traditionally, this is done
using data augmentation or hard-coding invariances into the architecture.
However, too much or too little invariance can hurt, and the correct amount is
unknown a priori and dependent on the instance. Ideally, the appropriate
invariance would be learned from data and inferred at test-time.
We treat invariance as a prediction problem. Given any image, we use a
normalizing flow to predict a distribution over transformations and average the
predictions over them. Since this distribution only depends on the instance, we
can align instances before classifying them and generalize invariance across
classes. The same distribution can also be used to adapt to out-of-distribution
poses. This normalizing flow is trained end-to-end and can learn a much larger
range of transformations than Augerino and InstaAug. When used as data
augmentation, our method shows accuracy and robustness gains on CIFAR 10,
CIFAR10-LT, and TinyImageNet. | [
"Utkarsh Singhal",
"Carlos Esteves",
"Ameesh Makadia",
"Stella X. Yu"
] | 2023-09-28 17:59:58 | http://arxiv.org/abs/2309.16672v1 | http://arxiv.org/pdf/2309.16672v1 | 2309.16672v1 |
RealFill: Reference-Driven Generation for Authentic Image Completion | Recent advances in generative imagery have brought forth outpainting and
inpainting models that can produce high-quality, plausible image content in
unknown regions, but the content these models hallucinate is necessarily
inauthentic, since the models lack sufficient context about the true scene. In
this work, we propose RealFill, a novel generative approach for image
completion that fills in missing regions of an image with the content that
should have been there. RealFill is a generative inpainting model that is
personalized using only a few reference images of a scene. These reference
images do not have to be aligned with the target image, and can be taken with
drastically varying viewpoints, lighting conditions, camera apertures, or image
styles. Once personalized, RealFill is able to complete a target image with
visually compelling contents that are faithful to the original scene. We
evaluate RealFill on a new image completion benchmark that covers a set of
diverse and challenging scenarios, and find that it outperforms existing
approaches by a large margin. See more results on our project page:
https://realfill.github.io | [
"Luming Tang",
"Nataniel Ruiz",
"Qinghao Chu",
"Yuanzhen Li",
"Aleksander Holynski",
"David E. Jacobs",
"Bharath Hariharan",
"Yael Pritch",
"Neal Wadhwa",
"Kfir Aberman",
"Michael Rubinstein"
] | 2023-09-28 17:59:29 | http://arxiv.org/abs/2309.16668v1 | http://arxiv.org/pdf/2309.16668v1 | 2309.16668v1 |
HyperPPO: A scalable method for finding small policies for robotic control | Models with fewer parameters are necessary for the neural control of
memory-limited, performant robots. Finding these smaller neural network
architectures can be time-consuming. We propose HyperPPO, an on-policy
reinforcement learning algorithm that utilizes graph hypernetworks to estimate
the weights of multiple neural architectures simultaneously. Our method
estimates weights for networks that are much smaller than those in common-use
networks yet encode highly performant policies. We obtain multiple trained
policies at the same time while maintaining sample efficiency and provide the
user the choice of picking a network architecture that satisfies their
computational constraints. We show that our method scales well - more training
resources produce faster convergence to higher-performing architectures. We
demonstrate that the neural policies estimated by HyperPPO are capable of
decentralized control of a Crazyflie2.1 quadrotor. Website:
https://sites.google.com/usc.edu/hyperppo | [
"Shashank Hegde",
"Zhehui Huang",
"Gaurav S. Sukhatme"
] | 2023-09-28 17:58:26 | http://arxiv.org/abs/2309.16663v1 | http://arxiv.org/pdf/2309.16663v1 | 2309.16663v1 |
Geodesic Regression Characterizes 3D Shape Changes in the Female Brain During Menstruation | Women are at higher risk of Alzheimer's and other neurological diseases after
menopause, and yet research connecting female brain health to sex hormone
fluctuations is limited. We seek to investigate this connection by developing
tools that quantify 3D shape changes that occur in the brain during sex hormone
fluctuations. Geodesic regression on the space of 3D discrete surfaces offers a
principled way to characterize the evolution of a brain's shape. However, in
its current form, this approach is too computationally expensive for practical
use. In this paper, we propose approximation schemes that accelerate geodesic
regression on shape spaces of 3D discrete surfaces. We also provide rules of
thumb for when each approximation can be used. We test our approach on
synthetic data to quantify the speed-accuracy trade-off of these approximations
and show that practitioners can expect very significant speed-up while only
sacrificing little accuracy. Finally, we apply the method to real brain shape
data and produce the first characterization of how the female hippocampus
changes shape during the menstrual cycle as a function of progesterone: a
characterization made (practically) possible by our approximation schemes. Our
work paves the way for comprehensive, practical shape analyses in the fields of
bio-medicine and computer vision. Our implementation is publicly available on
GitHub: https://github.com/bioshape-lab/my28brains. | [
"Adele Myers",
"Caitlin Taylor",
"Emily Jacobs",
"Nina Miolane"
] | 2023-09-28 17:58:19 | http://arxiv.org/abs/2309.16662v1 | http://arxiv.org/pdf/2309.16662v1 | 2309.16662v1 |
Memory in Plain Sight: A Survey of the Uncanny Resemblances between Diffusion Models and Associative Memories | Diffusion Models (DMs) have recently set state-of-the-art on many generation
benchmarks. However, there are myriad ways to describe them mathematically,
which makes it difficult to develop a simple understanding of how they work. In
this survey, we provide a concise overview of DMs from the perspective of
dynamical systems and Ordinary Differential Equations (ODEs) which exposes a
mathematical connection to the highly related yet often overlooked class of
energy-based models, called Associative Memories (AMs). Energy-based AMs are a
theoretical framework that behave much like denoising DMs, but they enable us
to directly compute a Lyapunov energy function on which we can perform gradient
descent to denoise data. We then summarize the 40 year history of energy-based
AMs, beginning with the original Hopfield Network, and discuss new research
directions for AMs and DMs that are revealed by characterizing the extent of
their similarities and differences | [
"Benjamin Hoover",
"Hendrik Strobelt",
"Dmitry Krotov",
"Judy Hoffman",
"Zsolt Kira",
"Duen Horng Chau"
] | 2023-09-28 17:57:09 | http://arxiv.org/abs/2309.16750v1 | http://arxiv.org/pdf/2309.16750v1 | 2309.16750v1 |
Discovering environments with XRM | Successful out-of-distribution generalization requires environment
annotations. Unfortunately, these are resource-intensive to obtain, and their
relevance to model performance is limited by the expectations and perceptual
biases of human annotators. Therefore, to enable robust AI systems across
applications, we must develop algorithms to automatically discover environments
inducing broad generalization. Current proposals, which divide examples based
on their training error, suffer from one fundamental problem. These methods add
hyper-parameters and early-stopping criteria that are impossible to tune
without a validation set with human-annotated environments, the very
information subject to discovery. In this paper, we propose
Cross-Risk-Minimization (XRM) to address this issue. XRM trains two twin
networks, each learning from one random half of the training data, while
imitating confident held-out mistakes made by its sibling. XRM provides a
recipe for hyper-parameter tuning, does not require early-stopping, and can
discover environments for all training and validation data. Domain
generalization algorithms built on top of XRM environments achieve oracle
worst-group-accuracy, solving a long-standing problem in out-of-distribution
generalization. | [
"Mohammad Pezeshki",
"Diane Bouchacourt",
"Mark Ibrahim",
"Nicolas Ballas",
"Pascal Vincent",
"David Lopez-Paz"
] | 2023-09-28 17:55:45 | http://arxiv.org/abs/2309.16748v1 | http://arxiv.org/pdf/2309.16748v1 | 2309.16748v1 |
Visual In-Context Learning for Few-Shot Eczema Segmentation | Automated diagnosis of eczema from digital camera images is crucial for
developing applications that allow patients to self-monitor their recovery. An
important component of this is the segmentation of eczema region from such
images. Current methods for eczema segmentation rely on deep neural networks
such as convolutional (CNN)-based U-Net or transformer-based Swin U-Net. While
effective, these methods require high volume of annotated data, which can be
difficult to obtain. Here, we investigate the capabilities of visual in-context
learning that can perform few-shot eczema segmentation with just a handful of
examples and without any need for retraining models. Specifically, we propose a
strategy for applying in-context learning for eczema segmentation with a
generalist vision model called SegGPT. When benchmarked on a dataset of
annotated eczema images, we show that SegGPT with just 2 representative example
images from the training dataset performs better (mIoU: 36.69) than a CNN U-Net
trained on 428 images (mIoU: 32.60). We also discover that using more number of
examples for SegGPT may in fact be harmful to its performance. Our result
highlights the importance of visual in-context learning in developing faster
and better solutions to skin imaging tasks. Our result also paves the way for
developing inclusive solutions that can cater to minorities in the demographics
who are typically heavily under-represented in the training data. | [
"Neelesh Kumar",
"Oya Aran",
"Venugopal Vasudevan"
] | 2023-09-28 17:55:24 | http://arxiv.org/abs/2309.16656v1 | http://arxiv.org/pdf/2309.16656v1 | 2309.16656v1 |
Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures | In, Elmarakeby et al., "Biologically informed deep neural network for
prostate cancer discovery", a feedforward neural network with biologically
informed, sparse connections (P-NET) was presented to model the state of
prostate cancer. We verified the reproducibility of the study conducted by
Elmarakeby et al., using both their original codebase, and our own
re-implementation using more up-to-date libraries. We quantified the
contribution of network sparsification by Reactome biological pathways, and
confirmed its importance to P-NET's superior performance. Furthermore, we
explored alternative neural architectures and approaches to incorporating
biological information into the networks. We experimented with three types of
graph neural networks on the same training data, and investigated the clinical
prediction agreement between different models. Our analyses demonstrated that
deep neural networks with distinct architectures make incorrect predictions for
individual patient that are persistent across different initializations of a
specific neural architecture. This suggests that different neural architectures
are sensitive to different aspects of the data, an important yet under-explored
challenge for clinical prediction tasks. | [
"Christian Pedersen",
"Tiberiu Tesileanu",
"Tinghui Wu",
"Siavash Golkar",
"Miles Cranmer",
"Zijun Zhang",
"Shirley Ho"
] | 2023-09-28 17:51:02 | http://arxiv.org/abs/2309.16645v1 | http://arxiv.org/pdf/2309.16645v1 | 2309.16645v1 |
Mixup Your Own Pairs | In representation learning, regression has traditionally received less
attention than classification. Directly applying representation learning
techniques designed for classification to regression often results in
fragmented representations in the latent space, yielding sub-optimal
performance. In this paper, we argue that the potential of contrastive learning
for regression has been overshadowed due to the neglect of two crucial aspects:
ordinality-awareness and hardness. To address these challenges, we advocate
"mixup your own contrastive pairs for supervised contrastive regression",
instead of relying solely on real/augmented samples. Specifically, we propose
Supervised Contrastive Learning for Regression with Mixup (SupReMix). It takes
anchor-inclusive mixtures (mixup of the anchor and a distinct negative sample)
as hard negative pairs and anchor-exclusive mixtures (mixup of two distinct
negative samples) as hard positive pairs at the embedding level. This strategy
formulates harder contrastive pairs by integrating richer ordinal information.
Through extensive experiments on six regression datasets including 2D images,
volumetric images, text, tabular data, and time-series signals, coupled with
theoretical analysis, we demonstrate that SupReMix pre-training fosters
continuous ordered representations of regression data, resulting in significant
improvement in regression performance. Furthermore, SupReMix is superior to
other approaches in a range of regression challenges including transfer
learning, imbalanced training data, and scenarios with fewer training samples. | [
"Yilei Wu",
"Zijian Dong",
"Chongyao Chen",
"Wangchunshu Zhou",
"Juan Helen Zhou"
] | 2023-09-28 17:38:59 | http://arxiv.org/abs/2309.16633v2 | http://arxiv.org/pdf/2309.16633v2 | 2309.16633v2 |
Robust Offline Reinforcement Learning -- Certify the Confidence Interval | Currently, reinforcement learning (RL), especially deep RL, has received more
and more attention in the research area. However, the security of RL has been
an obvious problem due to the attack manners becoming mature. In order to
defend against such adversarial attacks, several practical approaches are
developed, such as adversarial training, data filtering, etc. However, these
methods are mostly based on empirical algorithms and experiments, without
rigorous theoretical analysis of the robustness of the algorithms. In this
paper, we develop an algorithm to certify the robustness of a given policy
offline with random smoothing, which could be proven and conducted as
efficiently as ones without random smoothing. Experiments on different
environments confirm the correctness of our algorithm. | [
"Jiarui Yao",
"Simon Shaolei Du"
] | 2023-09-28 17:37:01 | http://arxiv.org/abs/2309.16631v2 | http://arxiv.org/pdf/2309.16631v2 | 2309.16631v2 |
Harnessing Diverse Data for Global Disaster Prediction: A Multimodal Framework | As climate change intensifies, the urgency for accurate global-scale disaster
predictions grows. This research presents a novel multimodal disaster
prediction framework, combining weather statistics, satellite imagery, and
textual insights. We particularly focus on "flood" and "landslide" predictions,
given their ties to meteorological and topographical factors. The model is
meticulously crafted based on the available data and we also implement
strategies to address class imbalance. While our findings suggest that
integrating multiple data sources can bolster model performance, the extent of
enhancement differs based on the specific nature of each disaster and their
unique underlying causes. | [
"Gengyin Liu",
"Huaiyang Zhong"
] | 2023-09-28 17:36:27 | http://arxiv.org/abs/2309.16747v1 | http://arxiv.org/pdf/2309.16747v1 | 2309.16747v1 |
On Learning with LAD | The logical analysis of data, LAD, is a technique that yields two-class
classifiers based on Boolean functions having disjunctive normal form (DNF)
representation. Although LAD algorithms employ optimization techniques, the
resulting binary classifiers or binary rules do not lead to overfitting. We
propose a theoretical justification for the absence of overfitting by
estimating the Vapnik-Chervonenkis dimension (VC dimension) for LAD models
where hypothesis sets consist of DNFs with a small number of cubic monomials.
We illustrate and confirm our observations empirically. | [
"C. A. Jothishwaran",
"Biplav Srivastava",
"Jitin Singla",
"Sugata Gangopadhyay"
] | 2023-09-28 17:35:26 | http://arxiv.org/abs/2309.16630v1 | http://arxiv.org/pdf/2309.16630v1 | 2309.16630v1 |
Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit | The cost of hyperparameter tuning in deep learning has been rising with model
sizes, prompting practitioners to find new tuning methods using a proxy of
smaller networks. One such proposal uses $\mu$P parameterized networks, where
the optimal hyperparameters for small width networks transfer to networks with
arbitrarily large width. However, in this scheme, hyperparameters do not
transfer across depths. As a remedy, we study residual networks with a residual
branch scale of $1/\sqrt{\text{depth}}$ in combination with the $\mu$P
parameterization. We provide experiments demonstrating that residual
architectures including convolutional ResNets and Vision Transformers trained
with this parameterization exhibit transfer of optimal hyperparameters across
width and depth on CIFAR-10 and ImageNet. Furthermore, our empirical findings
are supported and motivated by theory. Using recent developments in the
dynamical mean field theory (DMFT) description of neural network learning
dynamics, we show that this parameterization of ResNets admits a well-defined
feature learning joint infinite-width and infinite-depth limit and show
convergence of finite-size network dynamics towards this limit. | [
"Blake Bordelon",
"Lorenzo Noci",
"Mufan Bill Li",
"Boris Hanin",
"Cengiz Pehlevan"
] | 2023-09-28 17:20:50 | http://arxiv.org/abs/2309.16620v1 | http://arxiv.org/pdf/2309.16620v1 | 2309.16620v1 |
Exploiting Edge Features in Graphs with Fused Network Gromov-Wasserstein Distance | Pairwise comparison of graphs is key to many applications in Machine learning
ranging from clustering, kernel-based classification/regression and more
recently supervised graph prediction. Distances between graphs usually rely on
informative representations of these structured objects such as bag of
substructures or other graph embeddings. A recently popular solution consists
in representing graphs as metric measure spaces, allowing to successfully
leverage Optimal Transport, which provides meaningful distances allowing to
compare them: the Gromov-Wasserstein distances. However, this family of
distances overlooks edge attributes, which are essential for many structured
objects. In this work, we introduce an extension of Gromov-Wasserstein distance
for comparing graphs whose both nodes and edges have features. We propose novel
algorithms for distance and barycenter computation. We empirically show the
effectiveness of the novel distance in learning tasks where graphs occur in
either input space or output space, such as classification and graph
prediction. | [
"Junjie Yang",
"Matthieu Labeau",
"Florence d'Alché-Buc"
] | 2023-09-28 17:05:03 | http://arxiv.org/abs/2309.16604v1 | http://arxiv.org/pdf/2309.16604v1 | 2309.16604v1 |
Deep Learning Based Uplink Multi-User SIMO Beamforming Design | The advancement of fifth generation (5G) wireless communication networks has
created a greater demand for wireless resource management solutions that offer
high data rates, extensive coverage, minimal latency and energy-efficient
performance. Nonetheless, traditional approaches have shortcomings when it
comes to computational complexity and their ability to adapt to dynamic
conditions, creating a gap between theoretical analysis and the practical
execution of algorithmic solutions for managing wireless resources. Deep
learning-based techniques offer promising solutions for bridging this gap with
their substantial representation capabilities. We propose a novel unsupervised
deep learning framework, which is called NNBF, for the design of uplink receive
multi-user single input multiple output (MU-SIMO) beamforming. The primary
objective is to enhance the throughput by focusing on maximizing the sum-rate
while also offering computationally efficient solution, in contrast to
established conventional methods. We conduct experiments for several antenna
configurations. Our experimental results demonstrate that NNBF exhibits
superior performance compared to our baseline methods, namely, zero-forcing
beamforming (ZFBF) and minimum mean square error (MMSE) equalizer.
Additionally, NNBF is scalable to the number of single-antenna user equipments
(UEs) while baseline methods have significant computational burden due to
matrix pseudo-inverse operation. | [
"Cemil Vahapoglu",
"Timothy J. O'Shea",
"Tamoghna Roy",
"Sennur Ulukus"
] | 2023-09-28 17:04:41 | http://arxiv.org/abs/2309.16603v1 | http://arxiv.org/pdf/2309.16603v1 | 2309.16603v1 |
Cross-Prediction-Powered Inference | While reliable data-driven decision-making hinges on high-quality labeled
data, the acquisition of quality labels often involves laborious human
annotations or slow and expensive scientific measurements. Machine learning is
becoming an appealing alternative as sophisticated predictive techniques are
being used to quickly and cheaply produce large amounts of predicted labels;
e.g., predicted protein structures are used to supplement experimentally
derived structures, predictions of socioeconomic indicators from satellite
imagery are used to supplement accurate survey data, and so on. Since
predictions are imperfect and potentially biased, this practice brings into
question the validity of downstream inferences. We introduce cross-prediction:
a method for valid inference powered by machine learning. With a small labeled
dataset and a large unlabeled dataset, cross-prediction imputes the missing
labels via machine learning and applies a form of debiasing to remedy the
prediction inaccuracies. The resulting inferences achieve the desired error
probability and are more powerful than those that only leverage the labeled
data. Closely related is the recent proposal of prediction-powered inference,
which assumes that a good pre-trained model is already available. We show that
cross-prediction is consistently more powerful than an adaptation of
prediction-powered inference in which a fraction of the labeled data is split
off and used to train the model. Finally, we observe that cross-prediction
gives more stable conclusions than its competitors; its confidence intervals
typically have significantly lower variability. | [
"Tijana Zrnic",
"Emmanuel J. Candès"
] | 2023-09-28 17:01:58 | http://arxiv.org/abs/2309.16598v2 | http://arxiv.org/pdf/2309.16598v2 | 2309.16598v2 |
Transfer Learning for Bayesian Optimization on Heterogeneous Search Spaces | Bayesian optimization (BO) is a popular black-box function optimization
method, which makes sequential decisions based on a Bayesian model, typically a
Gaussian process (GP), of the function. To ensure the quality of the model,
transfer learning approaches have been developed to automatically design GP
priors by learning from observations on "training" functions. These training
functions are typically required to have the same domain as the "test" function
(black-box function to be optimized). In this paper, we introduce MPHD, a model
pre-training method on heterogeneous domains, which uses a neural net mapping
from domain-specific contexts to specifications of hierarchical GPs. MPHD can
be seamlessly integrated with BO to transfer knowledge across heterogeneous
search spaces. Our theoretical and empirical results demonstrate the validity
of MPHD and its superior performance on challenging black-box function
optimization tasks. | [
"Zhou Fan",
"Xinran Han",
"Zi Wang"
] | 2023-09-28 17:01:43 | http://arxiv.org/abs/2309.16597v1 | http://arxiv.org/pdf/2309.16597v1 | 2309.16597v1 |
Can LLMs Effectively Leverage Graph Structural Information: When and Why | This paper studies Large Language Models (LLMs) augmented with structured
data--particularly graphs--a crucial data modality that remains underexplored
in the LLM literature. We aim to understand when and why the incorporation of
structural information inherent in graph data can improve the prediction
performance of LLMs on node classification tasks with textual features. To
address the ``when'' question, we examine a variety of prompting methods for
encoding structural information, in settings where textual node features are
either rich or scarce. For the ``why'' questions, we probe into two potential
contributing factors to the LLM performance: data leakage and homophily. Our
exploration of these questions reveals that (i) LLMs can benefit from
structural information, especially when textual node features are scarce; (ii)
there is no substantial evidence indicating that the performance of LLMs is
significantly attributed to data leakage; and (iii) the performance of LLMs on
a target node is strongly positively related to the local homophily ratio of
the node\footnote{Codes and datasets are at:
\url{https://github.com/TRAIS-Lab/LLM-Structured-Data}}. | [
"Jin Huang",
"Xingjian Zhang",
"Qiaozhu Mei",
"Jiaqi Ma"
] | 2023-09-28 16:58:37 | http://arxiv.org/abs/2309.16595v2 | http://arxiv.org/pdf/2309.16595v2 | 2309.16595v2 |
Navigating Healthcare Insights: A Birds Eye View of Explainability with Knowledge Graphs | Knowledge graphs (KGs) are gaining prominence in Healthcare AI, especially in
drug discovery and pharmaceutical research as they provide a structured way to
integrate diverse information sources, enhancing AI system interpretability.
This interpretability is crucial in healthcare, where trust and transparency
matter, and eXplainable AI (XAI) supports decision making for healthcare
professionals. This overview summarizes recent literature on the impact of KGs
in healthcare and their role in developing explainable AI models. We cover KG
workflow, including construction, relationship extraction, reasoning, and their
applications in areas like Drug-Drug Interactions (DDI), Drug Target
Interactions (DTI), Drug Development (DD), Adverse Drug Reactions (ADR), and
bioinformatics. We emphasize the importance of making KGs more interpretable
through knowledge-infused learning in healthcare. Finally, we highlight
research challenges and provide insights for future directions. | [
"Satvik Garg",
"Shivam Parikh",
"Somya Garg"
] | 2023-09-28 16:57:03 | http://arxiv.org/abs/2309.16593v1 | http://arxiv.org/pdf/2309.16593v1 | 2309.16593v1 |
Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection | The primary bottleneck towards obtaining good recognition performance in IR
images is the lack of sufficient labeled training data, owing to the cost of
acquiring such data. Realizing that object detection methods for the RGB
modality are quite robust (at least for some commonplace classes, like person,
car, etc.), thanks to the giant training sets that exist, in this work we seek
to leverage cues from the RGB modality to scale object detectors to the IR
modality, while preserving model performance in the RGB modality. At the core
of our method, is a novel tensor decomposition method called TensorFact which
splits the convolution kernels of a layer of a Convolutional Neural Network
(CNN) into low-rank factor matrices, with fewer parameters than the original
CNN. We first pretrain these factor matrices on the RGB modality, for which
plenty of training data are assumed to exist and then augment only a few
trainable parameters for training on the IR modality to avoid over-fitting,
while encouraging them to capture complementary cues from those trained only on
the RGB modality. We validate our approach empirically by first assessing how
well our TensorFact decomposed network performs at the task of detecting
objects in RGB images vis-a-vis the original network and then look at how well
it adapts to IR images of the FLIR ADAS v1 dataset. For the latter, we train
models under scenarios that pose challenges stemming from data paucity. From
the experiments, we observe that: (i) TensorFact shows performance gains on RGB
images; (ii) further, this pre-trained model, when fine-tuned, outperforms a
standard state-of-the-art object detector on the FLIR ADAS v1 dataset by about
4% in terms of mAP 50 score. | [
"Manish Sharma",
"Moitreya Chatterjee",
"Kuan-Chuan Peng",
"Suhas Lohit",
"Michael Jones"
] | 2023-09-28 16:55:52 | http://arxiv.org/abs/2309.16592v1 | http://arxiv.org/pdf/2309.16592v1 | 2309.16592v1 |
A Design Toolbox for the Development of Collaborative Distributed Machine Learning Systems | To leverage data for the sufficient training of machine learning (ML) models
from multiple parties in a confidentiality-preserving way, various
collaborative distributed ML (CDML) system designs have been developed, for
example, to perform assisted learning, federated learning, and split learning.
CDML system designs show different traits, including high agent autonomy, ML
model confidentiality, and fault tolerance. Facing a wide variety of CDML
system designs with different traits, it is difficult for developers to design
CDML systems with traits that match use case requirements in a targeted way.
However, inappropriate CDML system designs may result in CDML systems failing
their envisioned purposes. We developed a CDML design toolbox that can guide
the development of CDML systems. Based on the CDML design toolbox, we present
CDML system archetypes with distinct key traits that can support the design of
CDML systems to meet use case requirements. | [
"David Jin",
"Niclas Kannengießer",
"Sascha Rank",
"Ali Sunyaev"
] | 2023-09-28 16:44:18 | http://arxiv.org/abs/2309.16584v2 | http://arxiv.org/pdf/2309.16584v2 | 2309.16584v2 |
M-OFDFT: Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning | Orbital-free density functional theory (OFDFT) is a quantum chemistry
formulation that has a lower cost scaling than the prevailing Kohn-Sham DFT,
which is increasingly desired for contemporary molecular research. However, its
accuracy is limited by the kinetic energy density functional, which is
notoriously hard to approximate for non-periodic molecular systems. In this
work, we propose M-OFDFT, an OFDFT approach capable of solving molecular
systems using a deep-learning functional model. We build the essential
nonlocality into the model, which is made affordable by the concise density
representation as expansion coefficients under an atomic basis. With techniques
to address unconventional learning challenges therein, M-OFDFT achieves a
comparable accuracy with Kohn-Sham DFT on a wide range of molecules untouched
by OFDFT before. More attractively, M-OFDFT extrapolates well to molecules much
larger than those in training, which unleashes the appealing scaling for
studying large molecules including proteins, representing an advancement of the
accuracy-efficiency trade-off frontier in quantum chemistry. | [
"He Zhang",
"Siyuan Liu",
"Jiacheng You",
"Chang Liu",
"Shuxin Zheng",
"Ziheng Lu",
"Tong Wang",
"Nanning Zheng",
"Bin Shao"
] | 2023-09-28 16:33:36 | http://arxiv.org/abs/2309.16578v1 | http://arxiv.org/pdf/2309.16578v1 | 2309.16578v1 |
Review of Machine Learning Methods for Additive Manufacturing of Functionally Graded Materials | Additive manufacturing has revolutionized the manufacturing of complex parts
by enabling direct material joining and offers several advantages such as
cost-effective manufacturing of complex parts, reducing manufacturing waste,
and opening new possibilities for manufacturing automation. One group of
materials for which additive manufacturing holds great potential for enhancing
component performance and properties is Functionally Graded Materials (FGMs).
FGMs are advanced composite materials that exhibit smoothly varying properties
making them desirable for applications in aerospace, automobile, biomedical,
and defense industries. Such composition differs from traditional composite
materials, since the location-dependent composition changes gradually in FGMs,
leading to enhanced properties. Recently, machine learning techniques have
emerged as a promising means for fabrication of FGMs through optimizing
processing parameters, improving product quality, and detecting manufacturing
defects. This paper first provides a brief literature review of works related
to FGM fabrication, followed by reviewing works on employing machine learning
in additive manufacturing, Afterward, we provide an overview of published works
in the literature related to the application of machine learning methods in
Directed Energy Deposition and for fabrication of FGMs. | [
"Mohammad Karimzadeh",
"Aleksandar Vakanski",
"Fei Xu",
"Xinchang Zhang"
] | 2023-09-28 16:27:07 | http://arxiv.org/abs/2309.16571v1 | http://arxiv.org/pdf/2309.16571v1 | 2309.16571v1 |
Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings | Unsupervised learning allows us to leverage unlabelled data, which has become
abundantly available, and to create embeddings that are usable on a variety of
downstream tasks. However, the typical lack of interpretability of unsupervised
representation learning has become a limiting factor with regard to recent
transparent-AI regulations. In this paper, we study graph representation
learning and we show that data augmentation that preserves semantics can be
learned and used to produce interpretations. Our framework, which we named
INGENIOUS, creates inherently interpretable embeddings and eliminates the need
for costly additional post-hoc analysis. We also introduce additional metrics
addressing the lack of formalism and metrics in the understudied area of
unsupervised-representation learning interpretability. Our results are
supported by an experimental study applied to both graph-level and node-level
tasks and show that interpretable embeddings provide state-of-the-art
performance on subsequent downstream tasks. | [
"Gregory Scafarto",
"Madalina Ciortan",
"Simon Tihon",
"Quentin Ferre"
] | 2023-09-28 16:21:40 | http://arxiv.org/abs/2309.16564v1 | http://arxiv.org/pdf/2309.16564v1 | 2309.16564v1 |
CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption | We investigate the regret-minimisation problem in a multi-armed bandit
setting with arbitrary corruptions. Similar to the classical setup, the agent
receives rewards generated independently from the distribution of the arm
chosen at each time. However, these rewards are not directly observed. Instead,
with a fixed $\varepsilon\in (0,\frac{1}{2})$, the agent observes a sample from
the chosen arm's distribution with probability $1-\varepsilon$, or from an
arbitrary corruption distribution with probability $\varepsilon$. Importantly,
we impose no assumptions on these corruption distributions, which can be
unbounded. In this setting, accommodating potentially unbounded corruptions, we
establish a problem-dependent lower bound on regret for a given family of arm
distributions. We introduce CRIMED, an asymptotically-optimal algorithm that
achieves the exact lower bound on regret for bandits with Gaussian
distributions with known variance. Additionally, we provide a finite-sample
analysis of CRIMED's regret performance. Notably, CRIMED can effectively handle
corruptions with $\varepsilon$ values as high as $\frac{1}{2}$. Furthermore, we
develop a tight concentration result for medians in the presence of arbitrary
corruptions, even with $\varepsilon$ values up to $\frac{1}{2}$, which may be
of independent interest. We also discuss an extension of the algorithm for
handling misspecification in Gaussian model. | [
"Shubhada Agrawal",
"Timothée Mathieu",
"Debabrota Basu",
"Odalric-Ambrym Maillard"
] | 2023-09-28 16:19:53 | http://arxiv.org/abs/2309.16563v1 | http://arxiv.org/pdf/2309.16563v1 | 2309.16563v1 |
Voting Network for Contour Levee Farmland Segmentation and Classification | High-resolution aerial imagery allows fine details in the segmentation of
farmlands. However, small objects and features introduce distortions to the
delineation of object boundaries, and larger contextual views are needed to
mitigate class confusion. In this work, we present an end-to-end trainable
network for segmenting farmlands with contour levees from high-resolution
aerial imagery. A fusion block is devised that includes multiple voting blocks
to achieve image segmentation and classification. We integrate the fusion block
with a backbone and produce both semantic predictions and segmentation slices.
The segmentation slices are used to perform majority voting on the predictions.
The network is trained to assign the most likely class label of a segment to
its pixels, learning the concept of farmlands rather than analyzing
constitutive pixels separately. We evaluate our method using images from the
National Agriculture Imagery Program. Our method achieved an average accuracy
of 94.34\%. Compared to the state-of-the-art methods, the proposed method
obtains an improvement of 6.96% and 2.63% in the F1 score on average. | [
"Abolfazl Meyarian",
"Xiaohui Yuan"
] | 2023-09-28 16:16:08 | http://arxiv.org/abs/2309.16561v1 | http://arxiv.org/pdf/2309.16561v1 | 2309.16561v1 |
Implicit Gaussian process representation of vector fields over arbitrary latent manifolds | Gaussian processes (GPs) are popular nonparametric statistical models for
learning unknown functions and quantifying the spatiotemporal uncertainty in
data. Recent works have extended GPs to model scalar and vector quantities
distributed over non-Euclidean domains, including smooth manifolds appearing in
numerous fields such as computer vision, dynamical systems, and neuroscience.
However, these approaches assume that the manifold underlying the data is
known, limiting their practical utility. We introduce RVGP, a generalisation of
GPs for learning vector signals over latent Riemannian manifolds. Our method
uses positional encoding with eigenfunctions of the connection Laplacian,
associated with the tangent bundle, readily derived from common graph-based
approximation of data. We demonstrate that RVGP possesses global regularity
over the manifold, which allows it to super-resolve and inpaint vector fields
while preserving singularities. Furthermore, we use RVGP to reconstruct
high-density neural dynamics derived from low-density EEG recordings in healthy
individuals and Alzheimer's patients. We show that vector field singularities
are important disease markers and that their reconstruction leads to a
comparable classification accuracy of disease states to high-density
recordings. Thus, our method overcomes a significant practical limitation in
experimental and clinical applications. | [
"Robert L. Peach",
"Matteo Vinao-Carl",
"Nir Grossman",
"Michael David",
"Emma Mallas",
"David Sharp",
"Paresh A. Malhotra",
"Pierre Vandergheynst",
"Adam Gosztolai"
] | 2023-09-28 16:02:39 | http://arxiv.org/abs/2309.16746v1 | http://arxiv.org/pdf/2309.16746v1 | 2309.16746v1 |
Correcting for heterogeneity in real-time epidemiological indicators | Auxiliary data sources have become increasingly important in epidemiological
surveillance, as they are often available at a finer spatial and temporal
resolution, larger coverage, and lower latency than traditional surveillance
signals. We describe the problem of spatial and temporal heterogeneity in these
signals derived from these data sources, where spatial and/or temporal biases
are present. We present a method to use a ``guiding'' signal to correct for
these biases and produce a more reliable signal that can be used for modeling
and forecasting. The method assumes that the heterogeneity can be approximated
by a low-rank matrix and that the temporal heterogeneity is smooth over time.
We also present a hyperparameter selection algorithm to choose the parameters
representing the matrix rank and degree of temporal smoothness of the
corrections. In the absence of ground truth, we use maps and plots to argue
that this method does indeed reduce heterogeneity. Reducing heterogeneity from
auxiliary data sources greatly increases their utility in modeling and
forecasting epidemics. | [
"Aaron Rumack",
"Roni Rosenfeld",
"F. William Townes"
] | 2023-09-28 15:57:18 | http://arxiv.org/abs/2309.16546v1 | http://arxiv.org/pdf/2309.16546v1 | 2309.16546v1 |
Unsupervised Fact Verification by Language Model Distillation | Unsupervised fact verification aims to verify a claim using evidence from a
trustworthy knowledge base without any kind of data annotation. To address this
challenge, algorithms must produce features for every claim that are both
semantically meaningful, and compact enough to find a semantic alignment with
the source information. In contrast to previous work, which tackled the
alignment problem by learning over annotated corpora of claims and their
corresponding labels, we propose SFAVEL (Self-supervised Fact Verification via
Language Model Distillation), a novel unsupervised framework that leverages
pre-trained language models to distil self-supervised features into
high-quality claim-fact alignments without the need for annotations. This is
enabled by a novel contrastive loss function that encourages features to attain
high-quality claim and evidence alignments whilst preserving the semantic
relationships across the corpora. Notably, we present results that achieve a
new state-of-the-art on the standard FEVER fact verification benchmark (+8%
accuracy) with linear evaluation. | [
"Adrián Bazaga",
"Pietro Liò",
"Gos Micklem"
] | 2023-09-28 15:53:44 | http://arxiv.org/abs/2309.16540v1 | http://arxiv.org/pdf/2309.16540v1 | 2309.16540v1 |
Uncertainty Quantification for Eosinophil Segmentation | Eosinophilic Esophagitis (EoE) is an allergic condition increasing in
prevalence. To diagnose EoE, pathologists must find 15 or more eosinophils
within a single high-power field (400X magnification). Determining whether or
not a patient has EoE can be an arduous process and any medical imaging
approaches used to assist diagnosis must consider both efficiency and
precision. We propose an improvement of Adorno et al's approach for quantifying
eosinphils using deep image segmentation. Our new approach leverages Monte
Carlo Dropout, a common approach in deep learning to reduce overfitting, to
provide uncertainty quantification on current deep learning models. The
uncertainty can be visualized in an output image to evaluate model performance,
provide insight to how deep learning algorithms function, and assist
pathologists in identifying eosinophils. | [
"Kevin Lin",
"Donald Brown",
"Sana Syed",
"Adam Greene"
] | 2023-09-28 15:49:01 | http://arxiv.org/abs/2309.16536v1 | http://arxiv.org/pdf/2309.16536v1 | 2309.16536v1 |
MotionLM: Multi-Agent Motion Forecasting as Language Modeling | Reliable forecasting of the future behavior of road agents is a critical
component to safe planning in autonomous vehicles. Here, we represent
continuous trajectories as sequences of discrete motion tokens and cast
multi-agent motion prediction as a language modeling task over this domain. Our
model, MotionLM, provides several advantages: First, it does not require
anchors or explicit latent variable optimization to learn multimodal
distributions. Instead, we leverage a single standard language modeling
objective, maximizing the average log probability over sequence tokens. Second,
our approach bypasses post-hoc interaction heuristics where individual agent
trajectory generation is conducted prior to interactive scoring. Instead,
MotionLM produces joint distributions over interactive agent futures in a
single autoregressive decoding process. In addition, the model's sequential
factorization enables temporally causal conditional rollouts. The proposed
approach establishes new state-of-the-art performance for multi-agent motion
prediction on the Waymo Open Motion Dataset, ranking 1st on the interactive
challenge leaderboard. | [
"Ari Seff",
"Brian Cera",
"Dian Chen",
"Mason Ng",
"Aurick Zhou",
"Nigamaa Nayakanti",
"Khaled S. Refaat",
"Rami Al-Rfou",
"Benjamin Sapp"
] | 2023-09-28 15:46:25 | http://arxiv.org/abs/2309.16534v1 | http://arxiv.org/pdf/2309.16534v1 | 2309.16534v1 |
Efficient Training of One Class Classification-SVMs | This study examines the use of a highly effective training method to conduct
one-class classification. The existence of both positive and negative examples
in the training data is necessary to develop an effective classifier in common
binary classification scenarios. Unfortunately, this criteria is not met in
many domains. Here, there is just one class of examples. Classification
algorithms that learn from solely positive input have been created to deal with
this setting. In this paper, an effective algorithm for dual soft-margin
one-class SVM training is presented. Our approach makes use of the Augmented
Lagrangian (AL-FPGM), a variant of the Fast Projected Gradient Method. The FPGM
requires only first derivatives, which for the dual soft margin OCC-SVM means
computing mainly a matrix-vector product. Therefore, AL-FPGM, being
computationally inexpensive, may complement existing quadratic programming
solvers for training large SVMs. We extensively validate our approach over
real-world datasets and demonstrate that our strategy obtains statistically
significant results. | [
"Isaac Amornortey Yowetu",
"Nana Kena Frempong"
] | 2023-09-28 15:35:16 | http://arxiv.org/abs/2309.16745v1 | http://arxiv.org/pdf/2309.16745v1 | 2309.16745v1 |
Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models | We propose a novel framework that combines deep generative time series models
with decision theory for generating personalized treatment strategies. It
leverages historical patient trajectory data to jointly learn the generation of
realistic personalized treatment and future outcome trajectories through deep
generative time series models. In particular, our framework enables the
generation of novel multivariate treatment strategies tailored to the
personalized patient history and trained for optimal expected future outcomes
based on conditional expected utility maximization. We demonstrate our
framework by generating personalized insulin treatment strategies and blood
glucose predictions for hospitalized diabetes patients, showcasing the
potential of our approach for generating improved personalized treatment
strategies. Keywords: deep generative model, probabilistic decision support,
personalized treatment generation, insulin and blood glucose prediction | [
"Manuel Schürch",
"Xiang Li",
"Ahmed Allam",
"Giulia Rathmes",
"Amina Mollaysa",
"Claudia Cavelti-Weder",
"Michael Krauthammer"
] | 2023-09-28 15:27:28 | http://arxiv.org/abs/2309.16521v1 | http://arxiv.org/pdf/2309.16521v1 | 2309.16521v1 |
AtomSurf : Surface Representation for Learning on Protein Structures | Recent advancements in Cryo-EM and protein structure prediction algorithms
have made large-scale protein structures accessible, paving the way for machine
learning-based functional annotations.The field of geometric deep learning
focuses on creating methods working on geometric data. An essential aspect of
learning from protein structures is representing these structures as a
geometric object (be it a grid, graph, or surface) and applying a learning
method tailored to this representation. The performance of a given approach
will then depend on both the representation and its corresponding learning
method.
In this paper, we investigate representing proteins as $\textit{3D mesh
surfaces}$ and incorporate them into an established representation benchmark.
Our first finding is that despite promising preliminary results, the surface
representation alone does not seem competitive with 3D grids. Building on this,
we introduce a synergistic approach, combining surface representations with
graph-based methods, resulting in a general framework that incorporates both
representations in learning. We show that using this combination, we are able
to obtain state-of-the-art results across $\textit{all tested tasks}$. Our code
and data can be found online: https://github.com/Vincentx15/atom2D . | [
"Vincent Mallet",
"Souhaib Attaiki",
"Maks Ovsjanikov"
] | 2023-09-28 15:25:17 | http://arxiv.org/abs/2309.16519v1 | http://arxiv.org/pdf/2309.16519v1 | 2309.16519v1 |
From Complexity to Clarity: Analytical Expressions of Deep Neural Network Weights via Clifford's Geometric Algebra and Convexity | In this paper, we introduce a novel analysis of neural networks based on
geometric (Clifford) algebra and convex optimization. We show that optimal
weights of deep ReLU neural networks are given by the wedge product of training
samples when trained with standard regularized loss. Furthermore, the training
problem reduces to convex optimization over wedge product features, which
encode the geometric structure of the training dataset. This structure is given
in terms of signed volumes of triangles and parallelotopes generated by data
vectors. The convex problem finds a small subset of samples via $\ell_1$
regularization to discover only relevant wedge product features. Our analysis
provides a novel perspective on the inner workings of deep neural networks and
sheds light on the role of the hidden layers. | [
"Mert Pilanci"
] | 2023-09-28 15:19:30 | http://arxiv.org/abs/2309.16512v1 | http://arxiv.org/pdf/2309.16512v1 | 2309.16512v1 |
Deep Single Models vs. Ensembles: Insights for a Fast Deployment of Parking Monitoring Systems | Searching for available parking spots in high-density urban centers is a
stressful task for drivers that can be mitigated by systems that know in
advance the nearest parking space available.
To this end, image-based systems offer cost advantages over other
sensor-based alternatives (e.g., ultrasonic sensors), requiring less physical
infrastructure for installation and maintenance.
Despite recent deep learning advances, deploying intelligent parking
monitoring is still a challenge since most approaches involve collecting and
labeling large amounts of data, which is laborious and time-consuming. Our
study aims to uncover the challenges in creating a global framework, trained
using publicly available labeled parking lot images, that performs accurately
across diverse scenarios, enabling the parking space monitoring as a
ready-to-use system to deploy in a new environment. Through exhaustive
experiments involving different datasets and deep learning architectures,
including fusion strategies and ensemble methods, we found that models trained
on diverse datasets can achieve 95\% accuracy without the burden of data
annotation and model training on the target parking lot | [
"Andre Gustavo Hochuli",
"Jean Paul Barddal",
"Gillian Cezar Palhano",
"Leonardo Matheus Mendes",
"Paulo Ricardo Lisboa de Almeida"
] | 2023-09-28 14:59:53 | http://arxiv.org/abs/2309.16495v1 | http://arxiv.org/pdf/2309.16495v1 | 2309.16495v1 |
Towards Poisoning Fair Representations | Fair machine learning seeks to mitigate model prediction bias against certain
demographic subgroups such as elder and female. Recently, fair representation
learning (FRL) trained by deep neural networks has demonstrated superior
performance, whereby representations containing no demographic information are
inferred from the data and then used as the input to classification or other
downstream tasks. Despite the development of FRL methods, their vulnerability
under data poisoning attack, a popular protocol to benchmark model robustness
under adversarial scenarios, is under-explored. Data poisoning attacks have
been developed for classical fair machine learning methods which incorporate
fairness constraints into shallow-model classifiers. Nonetheless, these attacks
fall short in FRL due to notably different fairness goals and model
architectures. This work proposes the first data poisoning framework attacking
FRL. We induce the model to output unfair representations that contain as much
demographic information as possible by injecting carefully crafted poisoning
samples into the training data. This attack entails a prohibitive bilevel
optimization, wherefore an effective approximated solution is proposed. A
theoretical analysis on the needed number of poisoning samples is derived and
sheds light on defending against the attack. Experiments on benchmark fairness
datasets and state-of-the-art fair representation learning models demonstrate
the superiority of our attack. | [
"Tianci Liu",
"Haoyu Wang",
"Feijie Wu",
"Hengtong Zhang",
"Pan Li",
"Lu Su",
"Jing Gao"
] | 2023-09-28 14:51:20 | http://arxiv.org/abs/2309.16487v1 | http://arxiv.org/pdf/2309.16487v1 | 2309.16487v1 |
Predicting Long-term Renal Impairment in Post-COVID-19 Patients with Machine Learning Algorithms | The COVID-19 pandemic has had far-reaching implications for global public
health. As we continue to grapple with its consequences, it becomes
increasingly clear that post-COVID-19 complications are a significant concern.
Among these complications, renal impairment has garnered particular attention
due to its potential long-term health impacts. This study, conducted with a
cohort of 821 post-COVID-19 patients from diverse regions of Iraq across the
years 2021, 2022, and 2023, endeavors to predict the risk of long-term renal
impairment using advanced machine learning algorithms. Our findings have the
potential to revolutionize post-COVID-19 patient care by enabling early
identification and intervention for those at risk of renal impairment,
ultimately improving clinical outcomes. This research encompasses comprehensive
data collection and preprocessing, feature selection, and the development of
predictive models using various machine learning algorithms. The study's
objectives are to assess the incidence of long-term renal impairment in
post-COVID-19 patients, identify associated risk factors, create predictive
models, and evaluate their accuracy. We anticipate that our machine learning
models, drawing from a rich dataset, will provide valuable insights into the
risk of renal impairment, ultimately enhancing patient care and quality of
life. In conclusion, the research presented herein offers a critical
contribution to the field of post-COVID-19 care. By harnessing the power of
machine learning, we aim to predict long-term renal impairment risk accurately.
These predictions have the potential to inform healthcare professionals,
enabling them to take proactive measures and provide targeted interventions for
post-COVID-19 patients at risk of renal complications, thus minimizing the
impact of this serious health concern. | [
"Maitham G. Yousif",
"Hector J. Castro",
"John Martin",
"Hayder A. Albaqer",
"Fadhil G. Al-Amran",
"Habeeb W. Shubber",
"Salman Rawaf"
] | 2023-09-28 14:44:06 | http://arxiv.org/abs/2309.16744v1 | http://arxiv.org/pdf/2309.16744v1 | 2309.16744v1 |
High-dimensional robust regression under heavy-tailed data: Asymptotics and Universality | We investigate the high-dimensional properties of robust regression
estimators in the presence of heavy-tailed contamination of both the covariates
and response functions. In particular, we provide a sharp asymptotic
characterisation of M-estimators trained on a family of elliptical covariate
and noise data distributions including cases where second and higher moments do
not exist. We show that, despite being consistent, the Huber loss with
optimally tuned location parameter $\delta$ is suboptimal in the
high-dimensional regime in the presence of heavy-tailed noise, highlighting the
necessity of further regularisation to achieve optimal performance. This result
also uncovers the existence of a curious transition in $\delta$ as a function
of the sample complexity and contamination. Moreover, we derive the decay rates
for the excess risk of ridge regression. We show that, while it is both optimal
and universal for noise distributions with finite second moment, its decay rate
can be considerably faster when the covariates' second moment does not exist.
Finally, we show that our formulas readily generalise to a richer family of
models and data distributions, such as generalised linear estimation with
arbitrary convex regularisation trained on mixture models. | [
"Urte Adomaityte",
"Leonardo Defilippis",
"Bruno Loureiro",
"Gabriele Sicuro"
] | 2023-09-28 14:39:50 | http://arxiv.org/abs/2309.16476v1 | http://arxiv.org/pdf/2309.16476v1 | 2309.16476v1 |
Compositional Program Generation for Systematic Generalization | Compositional generalization is a key ability of humans that enables us to
learn new concepts from only a handful examples. Machine learning models,
including the now ubiquitous transformers, struggle to generalize in this way,
and typically require thousands of examples of a concept during training in
order to generalize meaningfully. This difference in ability between humans and
artificial neural architectures, motivates this study on a neuro-symbolic
architecture called the Compositional Program Generator (CPG). CPG has three
key features: modularity, type abstraction, and recursive composition, that
enable it to generalize both systematically to new concepts in a few-shot
manner, as well as productively by length on various sequence-to-sequence
language tasks. For each input, CPG uses a grammar of the input domain and a
parser to generate a type hierarchy in which each grammar rule is assigned its
own unique semantic module, a probabilistic copy or substitution program.
Instances with the same hierarchy are processed with the same composed program,
while those with different hierarchies may be processed with different
programs. CPG learns parameters for the semantic modules and is able to learn
the semantics for new types incrementally. Given a context-free grammar of the
input language and a dictionary mapping each word in the source language to its
interpretation in the output language, CPG can achieve perfect generalization
on the SCAN and COGS benchmarks, in both standard and extreme few-shot
settings. | [
"Tim Klinger",
"Luke Liu",
"Soham Dan",
"Maxwell Crouse",
"Parikshit Ram",
"Alexander Gray"
] | 2023-09-28 14:33:20 | http://arxiv.org/abs/2309.16467v1 | http://arxiv.org/pdf/2309.16467v1 | 2309.16467v1 |
A Metaheuristic for Amortized Search in High-Dimensional Parameter Spaces | Parameter inference for dynamical models of (bio)physical systems remains a
challenging problem. Intractable gradients, high-dimensional spaces, and
non-linear model functions are typically problematic without large
computational budgets. A recent body of work in that area has focused on
Bayesian inference methods, which consider parameters under their statistical
distributions and therefore, do not derive point estimates of optimal parameter
values. Here we propose a new metaheuristic that drives dimensionality
reductions from feature-informed transformations (DR-FFIT) to address these
bottlenecks. DR-FFIT implements an efficient sampling strategy that facilitates
a gradient-free parameter search in high-dimensional spaces. We use artificial
neural networks to obtain differentiable proxies for the model's features of
interest. The resulting gradients enable the estimation of a local active
subspace of the model within a defined sampling region. This approach enables
efficient dimensionality reductions of highly non-linear search spaces at a low
computational cost. Our test data show that DR-FFIT boosts the performances of
random-search and simulated-annealing against well-established metaheuristics,
and improves the goodness-of-fit of the model, all within contained run-time
costs. | [
"Dominic Boutet",
"Sylvain Baillet"
] | 2023-09-28 14:25:14 | http://arxiv.org/abs/2309.16465v1 | http://arxiv.org/pdf/2309.16465v1 | 2309.16465v1 |
Augmenting LLMs with Knowledge: A survey on hallucination prevention | Large pre-trained language models have demonstrated their proficiency in
storing factual knowledge within their parameters and achieving remarkable
results when fine-tuned for downstream natural language processing tasks.
Nonetheless, their capacity to access and manipulate knowledge with precision
remains constrained, resulting in performance disparities on
knowledge-intensive tasks when compared to task-specific architectures.
Additionally, the challenges of providing provenance for model decisions and
maintaining up-to-date world knowledge persist as open research frontiers. To
address these limitations, the integration of pre-trained models with
differentiable access mechanisms to explicit non-parametric memory emerges as a
promising solution. This survey delves into the realm of language models (LMs)
augmented with the ability to tap into external knowledge sources, including
external knowledge bases and search engines. While adhering to the standard
objective of predicting missing tokens, these augmented LMs leverage diverse,
possibly non-parametric external modules to augment their contextual processing
capabilities, departing from the conventional language modeling paradigm.
Through an exploration of current advancements in augmenting large language
models with knowledge, this work concludes that this emerging research
direction holds the potential to address prevalent issues in traditional LMs,
such as hallucinations, un-grounded responses, and scalability challenges. | [
"Konstantinos Andriopoulos",
"Johan Pouwelse"
] | 2023-09-28 14:09:58 | http://arxiv.org/abs/2309.16459v1 | http://arxiv.org/pdf/2309.16459v1 | 2309.16459v1 |
Universal Sleep Decoder: Aligning awake and sleep neural representation across subjects | Decoding memory content from brain activity during sleep has long been a goal
in neuroscience. While spontaneous reactivation of memories during sleep in
rodents is known to support memory consolidation and offline learning,
capturing memory replay in humans is challenging due to the absence of
well-annotated sleep datasets and the substantial differences in neural
patterns between wakefulness and sleep. To address these challenges, we
designed a novel cognitive neuroscience experiment and collected a
comprehensive, well-annotated electroencephalography (EEG) dataset from 52
subjects during both wakefulness and sleep. Leveraging this benchmark dataset,
we developed the Universal Sleep Decoder (USD) to align neural representations
between wakefulness and sleep across subjects. Our model achieves up to 16.6%
top-1 zero-shot accuracy on unseen subjects, comparable to decoding
performances using individual sleep data. Furthermore, fine-tuning USD on test
subjects enhances decoding accuracy to 25.9% top-1 accuracy, a substantial
improvement over the baseline chance of 6.7%. Model comparison and ablation
analyses reveal that our design choices, including the use of (i) an additional
contrastive objective to integrate awake and sleep neural signals and (ii) the
pretrain-finetune paradigm to incorporate different subjects, significantly
contribute to these performances. Collectively, our findings and methodologies
represent a significant advancement in the field of sleep decoding. | [
"Hui Zheng",
"Zhongtao Chen",
"Haiteng Wang",
"Jianyang Zhou",
"Lin Zheng",
"Yunzhe Liu"
] | 2023-09-28 14:06:34 | http://arxiv.org/abs/2309.16457v1 | http://arxiv.org/pdf/2309.16457v1 | 2309.16457v1 |
Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective | Existing approaches defend against backdoor attacks in federated learning
(FL) mainly through a) mitigating the impact of infected models, or b)
excluding infected models. The former negatively impacts model accuracy, while
the latter usually relies on globally clear boundaries between benign and
infected model updates. However, model updates are easy to be mixed and
scattered throughout in reality due to the diverse distributions of local data.
This work focuses on excluding infected models in FL. Unlike previous
perspectives from a global view, we propose Snowball, a novel anti-backdoor FL
framework through bidirectional elections from an individual perspective
inspired by one principle deduced by us and two principles in FL and deep
learning. It is characterized by a) bottom-up election, where each candidate
model update votes to several peer ones such that a few model updates are
elected as selectees for aggregation; and b) top-down election, where selectees
progressively enlarge themselves through picking up from the candidates. We
compare Snowball with state-of-the-art defenses to backdoor attacks in FL on
five real-world datasets, demonstrating its superior resistance to backdoor
attacks and slight impact on the accuracy of the global model. | [
"Zhen Qin",
"Feiyi Chen",
"Chen Zhi",
"Xueqiang Yan",
"Shuiguang Deng"
] | 2023-09-28 14:06:17 | http://arxiv.org/abs/2309.16456v1 | http://arxiv.org/pdf/2309.16456v1 | 2309.16456v1 |
On the Trade-offs between Adversarial Robustness and Actionable Explanations | As machine learning models are increasingly being employed in various
high-stakes settings, it becomes important to ensure that predictions of these
models are not only adversarially robust, but also readily explainable to
relevant stakeholders. However, it is unclear if these two notions can be
simultaneously achieved or if there exist trade-offs between them. In this
work, we make one of the first attempts at studying the impact of adversarially
robust models on actionable explanations which provide end users with a means
for recourse. We theoretically and empirically analyze the cost (ease of
implementation) and validity (probability of obtaining a positive model
prediction) of recourses output by state-of-the-art algorithms when the
underlying models are adversarially robust vs. non-robust. More specifically,
we derive theoretical bounds on the differences between the cost and the
validity of the recourses generated by state-of-the-art algorithms for
adversarially robust vs. non-robust linear and non-linear models. Our empirical
results with multiple real-world datasets validate our theoretical results and
show the impact of varying degrees of model robustness on the cost and validity
of the resulting recourses. Our analyses demonstrate that adversarially robust
models significantly increase the cost and reduce the validity of the resulting
recourses, thus shedding light on the inherent trade-offs between adversarial
robustness and actionable explanations | [
"Satyapriya Krishna",
"Chirag Agarwal",
"Himabindu Lakkaraju"
] | 2023-09-28 13:59:50 | http://arxiv.org/abs/2309.16452v1 | http://arxiv.org/pdf/2309.16452v1 | 2309.16452v1 |
A parsimonious, computationally efficient machine learning method for spatial regression | We introduce the modified planar rotator method (MPRS), a physically inspired
machine learning method for spatial/temporal regression. MPRS is a
non-parametric model which incorporates spatial or temporal correlations via
short-range, distance-dependent ``interactions'' without assuming a specific
form for the underlying probability distribution. Predictions are obtained by
means of a fully autonomous learning algorithm which employs equilibrium
conditional Monte Carlo simulations. MPRS is able to handle scattered data and
arbitrary spatial dimensions. We report tests on various synthetic and
real-word data in one, two and three dimensions which demonstrate that the MPRS
prediction performance (without parameter tuning) is competitive with standard
interpolation methods such as ordinary kriging and inverse distance weighting.
In particular, MPRS is a particularly effective gap-filling method for rough
and non-Gaussian data (e.g., daily precipitation time series). MPRS shows
superior computational efficiency and scalability for large samples. Massive
data sets involving millions of nodes can be processed in a few seconds on a
standard personal computer. | [
"Milan Žukovič",
"Dionissios T. Hristopulos"
] | 2023-09-28 13:57:36 | http://arxiv.org/abs/2309.16448v1 | http://arxiv.org/pdf/2309.16448v1 | 2309.16448v1 |
Diverse and Aligned Audio-to-Video Generation via Text-to-Video Model Adaptation | We consider the task of generating diverse and realistic videos guided by
natural audio samples from a wide variety of semantic classes. For this task,
the videos are required to be aligned both globally and temporally with the
input audio: globally, the input audio is semantically associated with the
entire output video, and temporally, each segment of the input audio is
associated with a corresponding segment of that video. We utilize an existing
text-conditioned video generation model and a pre-trained audio encoder model.
The proposed method is based on a lightweight adaptor network, which learns to
map the audio-based representation to the input representation expected by the
text-to-video generation model. As such, it also enables video generation
conditioned on text, audio, and, for the first time as far as we can ascertain,
on both text and audio. We validate our method extensively on three datasets
demonstrating significant semantic diversity of audio-video samples and further
propose a novel evaluation metric (AV-Align) to assess the alignment of
generated videos with input audio samples. AV-Align is based on the detection
and comparison of energy peaks in both modalities. In comparison to recent
state-of-the-art approaches, our method generates videos that are better
aligned with the input sound, both with respect to content and temporal axis.
We also show that videos produced by our method present higher visual quality
and are more diverse. | [
"Guy Yariv",
"Itai Gat",
"Sagie Benaim",
"Lior Wolf",
"Idan Schwartz",
"Yossi Adi"
] | 2023-09-28 13:26:26 | http://arxiv.org/abs/2309.16429v1 | http://arxiv.org/pdf/2309.16429v1 | 2309.16429v1 |
Nonlinear MPC design for incrementally ISS systems with application to GRU networks | This brief addresses the design of a Nonlinear Model Predictive Control
(NMPC) strategy for exponentially incremental Input-to-State Stable (ISS)
systems. In particular, a novel formulation is devised, which does not
necessitate the onerous computation of terminal ingredients, but rather relies
on the explicit definition of a minimum prediction horizon ensuring closed-loop
stability. The designed methodology is particularly suited for the control of
systems learned by Recurrent Neural Networks (RNNs), which are known for their
enhanced modeling capabilities and for which the incremental ISS properties can
be studied thanks to simple algebraic conditions. The approach is applied to
Gated Recurrent Unit (GRU) networks, providing also a method for the design of
a tailored state observer with convergence guarantees. The resulting control
architecture is tested on a benchmark system, demonstrating its good control
performances and efficient applicability. | [
"Fabio Bonassi",
"Alessio La Bella",
"Marcello Farina",
"Riccardo Scattolini"
] | 2023-09-28 13:26:20 | http://arxiv.org/abs/2309.16428v1 | http://arxiv.org/pdf/2309.16428v1 | 2309.16428v1 |
AutoCLIP: Auto-tuning Zero-Shot Classifiers for Vision-Language Models | Classifiers built upon vision-language models such as CLIP have shown
remarkable zero-shot performance across a broad range of image classification
tasks. Prior work has studied different ways of automatically creating
descriptor sets for every class based on prompt templates, ranging from
manually engineered templates over templates obtained from a large language
model to templates built from random words and characters. Up until now,
deriving zero-shot classifiers from the respective encoded class descriptors
has remained nearly unchanged, i.e., classify to the class that maximizes
cosine similarity between its averaged encoded class descriptors and the image
encoding. However, weighing all class descriptors equally can be suboptimal
when certain descriptors match visual clues on a given image better than
others. In this work, we propose AutoCLIP, a method for auto-tuning zero-shot
classifiers. AutoCLIP tunes per-image weights to each prompt template at
inference time, based on statistics of class descriptor-image similarities.
AutoCLIP is fully unsupervised, has very low computational overhead, and can be
easily implemented in few lines of code. We show that AutoCLIP outperforms
baselines across a broad range of vision-language models, datasets, and prompt
templates consistently and by up to 3 percent point accuracy. | [
"Jan Hendrik Metzen",
"Piyapat Saranrittichai",
"Chaithanya Kumar Mummadi"
] | 2023-09-28 13:08:08 | http://arxiv.org/abs/2309.16414v2 | http://arxiv.org/pdf/2309.16414v2 | 2309.16414v2 |
Selective Nonparametric Regression via Testing | Prediction with the possibility of abstention (or selective prediction) is an
important problem for error-critical machine learning applications. While
well-studied in the classification setup, selective approaches to regression
are much less developed. In this work, we consider the nonparametric
heteroskedastic regression problem and develop an abstention procedure via
testing the hypothesis on the value of the conditional variance at a given
point. Unlike existing methods, the proposed one allows to account not only for
the value of the variance itself but also for the uncertainty of the
corresponding variance predictor. We prove non-asymptotic bounds on the risk of
the resulting estimator and show the existence of several different convergence
regimes. Theoretical analysis is illustrated with a series of experiments on
simulated and real-world data. | [
"Fedor Noskov",
"Alexander Fishkov",
"Maxim Panov"
] | 2023-09-28 13:04:11 | http://arxiv.org/abs/2309.16412v1 | http://arxiv.org/pdf/2309.16412v1 | 2309.16412v1 |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.