b'Yisen Wang'

research

∙ 06/07/2023

On the Generalization of Multi-modal Contrastive Learning

Multi-modal contrastive learning (MMCL) has recently garnered considerab...

0 Qi Zhang, et al. ∙

research

∙ 03/24/2023

Generalist: Decoupling Natural and Robust Generalization

Deep neural networks obtained by standard training have been constantly ...

0 Hongjun Wang, et al. ∙

research

∙ 03/12/2023

ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond

Oversmoothing is a common phenomenon in a wide range of Graph Neural Net...

1 Xiaojun Guo, et al. ∙

research

∙ 03/08/2023

A Message Passing Perspective on Learning Dynamics of Contrastive Learning

In recent years, contrastive learning achieves impressive results on sel...

8 Yifei Wang, et al. ∙

research

∙ 03/04/2023

Towards a Unified Theoretical Understanding of Non-contrastive Learning via Rank Differential Mechanism

Recently, a variety of methods under the name of non-contrastive learnin...

0 Zhijian Zhuo, et al. ∙

research

∙ 03/02/2023

Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning

Recent works have shown that self-supervised learning can achieve remark...

9 Rundong Luo, et al. ∙

research

∙ 02/28/2023

Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks

Spiking Neural Networks (SNNs) are promising energy-efficient models for...

0 Qingyan Meng, et al. ∙

research

∙ 02/01/2023

SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks

Spiking neural networks (SNNs) with event-based computation are promisin...

0 Mingqing Xiao, et al. ∙

research

∙ 12/18/2022

On the Connection between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization

Despite impressive success in many tasks, deep learning models are shown...

0 Shiji Xin, et al. ∙

research

∙ 10/15/2022

How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders

Masked Autoencoders (MAE) based on a reconstruction task have risen to b...

5 Qi Zhang, et al. ∙

research

∙ 10/14/2022

When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture

Vision Transformers (ViTs) have recently achieved competitive performanc...

0 Yichuan Mo, et al. ∙

research

∙ 10/13/2022

Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors

Deep models often fail to generalize well in test domains when the data ...

0 Qixun Wang, et al. ∙

research

∙ 10/11/2022

Universal cover-time distribution of heterogeneous random walks

The cover-time problem, i.e., time to visit every site in a system, is o...

0 Jia-Qi Dong, et al. ∙

research

∙ 07/24/2022

Proving Common Mechanisms Shared by Twelve Methods of Boosting Adversarial Transferability

Although many methods have been proposed to enhance the transferability ...

0 Quanshi Zhang, et al. ∙

research

∙ 06/29/2022

Optimization-Induced Graph Implicit Nonlinear Diffusion

Due to the over-smoothing issue, most existing graph neural networks can...

3 Qi Chen, et al. ∙

research

∙ 05/01/2022

Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation

Spiking Neural Network (SNN) is a promising energy-efficient AI model wh...

0 Qingyan Meng, et al. ∙

research

∙ 03/25/2022

Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap

Recently, contrastive learning has risen to be a promising approach for ...

6 Yifei Wang, et al. ∙

research

∙ 03/25/2022

A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training

Adversarial Training (AT) is known as an effective approach to enhance t...

3 Yifei Wang, et al. ∙

research

∙ 03/18/2022

Self-Ensemble Adversarial Training for Improved Robustness

Due to numerous breakthroughs in real-world applications brought by mach...

0 Hongjun Wang, et al. ∙

research

∙ 12/15/2021

On the Convergence and Robustness of Adversarial Training

Improving the robustness of deep neural networks (DNNs) to adversarial e...

8 Yisen Wang, et al. ∙

research

∙ 11/25/2021

Clustering Effect of (Linearized) Adversarial Robust Models

Adversarial robustness has received increasing attention along with the ...

0 Yang Bai, et al. ∙

research

∙ 11/19/2021

Fooling Adversarial Training with Inducing Noise

Adversarial training is widely believed to be a reliable approach to imp...

0 Zhirui Wang, et al. ∙

research

∙ 11/15/2021

Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks

One major problem in black-box adversarial attacks is the high query com...

0 Chen Ma, et al. ∙

research

∙ 11/09/2021

On Training Implicit Models

This paper focuses on training implicit models of infinite layers. Speci...

8 Zhengyang Geng, et al. ∙

research

∙ 11/05/2021

A Unified Game-Theoretic Interpretation of Adversarial Robustness

This paper provides a unified view to explain different adversarial atta...

0 Jie Ren, et al. ∙

research

∙ 10/28/2021

Residual Relaxation for Multi-view Representation Learning

Multi-view methods learn representations by aligning multiple views of t...

4 Yifei Wang, et al. ∙

research

∙ 10/27/2021

Adversarial Neuron Pruning Purifies Backdoored Deep Models

As deep neural networks (DNNs) are growing larger, their requirements fo...

0 Dongxian Wu, et al. ∙

research

∙ 10/20/2021

Moiré Attack (MA): A New Potential Risk of Screen Photos

Images, captured by a camera, play a critical role in training Deep Neur...

2 Dantong Niu, et al. ∙

research

∙ 10/07/2021

Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks

Deep neural networks (DNNs) are known to be vulnerable to adversarial at...

0 Hanxun Huang, et al. ∙

research

∙ 10/07/2021

Generalization in Deep RL for TSP Problems via Equivariance and Local Search

Deep reinforcement learning (RL) has proved to be a competitive heuristi...

0 Wenbin Ouyang, et al. ∙

research

∙ 10/06/2021

Improving Generalization of Deep Reinforcement Learning-based TSP Solvers

Recent work applying deep reinforcement learning (DRL) to solve travelin...

0 Wenbin Ouyang, et al. ∙

research

∙ 09/29/2021

Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State

Spiking neural networks (SNNs) are brain-inspired models that enable ene...

0 Mingqing Xiao, et al. ∙

research

∙ 07/01/2021

Reparameterized Sampling for Generative Adversarial Networks

Recently, sampling methods have been successfully applied to enhance the...

0 Yifei Wang, et al. ∙

research

∙ 06/10/2021

GBHT: Gradient Boosting Histogram Transform for Density Estimation

In this paper, we propose a density estimation algorithm called Gradient...

0 Jingyi Cui, et al. ∙

research

∙ 06/10/2021

Leveraged Weighted Loss for Partial Label Learning

As an important branch of weakly supervised learning, partial label lear...

0 Hongwei Wen, et al. ∙

research

∙ 06/05/2021

Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?

Can models with particular structure avoid being biased towards spurious...

0 Dinghuai Zhang, et al. ∙

research

∙ 05/29/2021

Analysis and Applications of Class-wise Robustness in Adversarial Training

Adversarial training is one of the most effective approaches to improve ...

0 Qi Tian, et al. ∙

research

∙ 05/27/2021

Optimization Induced Equilibrium Networks

Implicit equilibrium models, i.e., deep neural networks (DNNs) defined b...

0 Xingyu Xie, et al. ∙

research

∙ 03/12/2021

Game-theoretic Understanding of Adversarially Learned Features

This paper aims to understand adversarial attacks and defense from a new...

0 Jie Ren, et al. ∙

research

∙ 03/11/2021

Improving Adversarial Robustness via Channel-wise Activation Suppressing

The study of adversarial examples and their activation has attracted sig...

0 Yang Bai, et al. ∙

research

∙ 02/22/2021

Dissecting the Diffusion Process in Linear Graph Convolutional Networks

Graph Convolutional Networks (GCNs) have attracted more and more attenti...

0 Yifei Wang, et al. ∙

research

∙ 01/18/2021

What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space

Deep neural networks (DNNs) have been widely adopted in different applic...

16 Shihao Zhao, et al. ∙

research

∙ 01/17/2021

Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions

Understanding the actions of both humans and artificial intelligence (AI...

0 Nodens Koren, et al. ∙

research

∙ 01/13/2021

Unlearnable Examples: Making Personal Data Unexploitable

The volume of "free" data on the internet has been key to the current su...

0 Hanxun Huang, et al. ∙

research

∙ 10/08/2020

A Unified Approach to Interpreting and Boosting Adversarial Transferability

In this paper, we use the interaction inside adversarial perturbations t...

0 Xin Wang, et al. ∙

research

∙ 09/24/2020

Improving Query Efficiency of Black-box Adversarial Attack

Deep neural networks (DNNs) have demonstrated excellent performance on v...

11 Yang Bai, et al. ∙

research

∙ 07/01/2020

Temporal Calibrated Regularization for Robust Noisy Label Learning

Deep neural networks (DNNs) exhibit great success on many tasks with the...

0 Dongxian Wu, et al. ∙

research

∙ 06/24/2020

Normalized Loss Functions for Deep Learning with Noisy Labels

Robust loss functions are essential for training accurate deep neural ne...

49 Xingjun Ma, et al. ∙

research

∙ 04/13/2020

Revisiting Loss Landscape for Adversarial Robustness

The study on improving the robustness of deep neural networks against ad...

0 Dongxian Wu, et al. ∙

research

∙ 02/14/2020

Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets

Skip connections are an essential component of current state-of-the-art ...

5 Dongxian Wu, et al. ∙

Yisen Wang

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