research
∙
08/29/2023
Robust Long-Tailed Learning via Label-Aware Bounded CVaR
Data in the real-world classification problems are always imbalanced or ...
share
research
∙
06/07/2023
On the Generalization of Multi-modal Contrastive Learning
Multi-modal contrastive learning (MMCL) has recently garnered considerab...
research
∙
03/24/2023
Generalist: Decoupling Natural and Robust Generalization
Deep neural networks obtained by standard training have been constantly ...
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...
research
∙
03/08/2023
A Message Passing Perspective on Learning Dynamics of Contrastive Learning
In recent years, contrastive learning achieves impressive results on sel...
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...
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...
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...
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...
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...
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...
research
∙
10/14/2022
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture
Vision Transformers (ViTs) have recently achieved competitive performanc...
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 ...
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...
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 ...
research
∙
06/29/2022
Optimization-Induced Graph Implicit Nonlinear Diffusion
Due to the over-smoothing issue, most existing graph neural networks can...
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...
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 ...
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...
research
∙
03/18/2022
Self-Ensemble Adversarial Training for Improved Robustness
Due to numerous breakthroughs in real-world applications brought by mach...
research
∙
12/15/2021
On the Convergence and Robustness of Adversarial Training
Improving the robustness of deep neural networks (DNNs) to adversarial e...
research
∙
11/25/2021
Clustering Effect of (Linearized) Adversarial Robust Models
Adversarial robustness has received increasing attention along with the ...
research
∙
11/19/2021
Fooling Adversarial Training with Inducing Noise
Adversarial training is widely believed to be a reliable approach to imp...
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...
research
∙
11/09/2021
On Training Implicit Models
This paper focuses on training implicit models of infinite layers. Speci...
research
∙
11/05/2021
A Unified Game-Theoretic Interpretation of Adversarial Robustness
This paper provides a unified view to explain different adversarial atta...
research
∙
10/28/2021
Residual Relaxation for Multi-view Representation Learning
Multi-view methods learn representations by aligning multiple views of t...
research
∙
10/27/2021
Adversarial Neuron Pruning Purifies Backdoored Deep Models
As deep neural networks (DNNs) are growing larger, their requirements fo...
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...
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...
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...
research
∙
10/06/2021
Improving Generalization of Deep Reinforcement Learning-based TSP Solvers
Recent work applying deep reinforcement learning (DRL) to solve travelin...
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...
research
∙
07/01/2021
Reparameterized Sampling for Generative Adversarial Networks
Recently, sampling methods have been successfully applied to enhance the...
research
∙
06/10/2021
GBHT: Gradient Boosting Histogram Transform for Density Estimation
In this paper, we propose a density estimation algorithm called Gradient...
research
∙
06/10/2021
Leveraged Weighted Loss for Partial Label Learning
As an important branch of weakly supervised learning, partial label lear...
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...
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 ...
research
∙
05/27/2021
Optimization Induced Equilibrium Networks
Implicit equilibrium models, i.e., deep neural networks (DNNs) defined b...
research
∙
03/12/2021
Game-theoretic Understanding of Adversarially Learned Features
This paper aims to understand adversarial attacks and defense from a new...
research
∙
03/11/2021
Improving Adversarial Robustness via Channel-wise Activation Suppressing
The study of adversarial examples and their activation has attracted sig...
research
∙
02/22/2021
Dissecting the Diffusion Process in Linear Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have attracted more and more attenti...
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...
research
∙
01/17/2021
Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions
Understanding the actions of both humans and artificial intelligence (AI...
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...
research
∙
10/08/2020
A Unified Approach to Interpreting and Boosting Adversarial Transferability
In this paper, we use the interaction inside adversarial perturbations t...
research
∙
09/24/2020
Improving Query Efficiency of Black-box Adversarial Attack
Deep neural networks (DNNs) have demonstrated excellent performance on v...
research
∙
07/01/2020
Temporal Calibrated Regularization for Robust Noisy Label Learning
Deep neural networks (DNNs) exhibit great success on many tasks with the...
research
∙
06/24/2020
Normalized Loss Functions for Deep Learning with Noisy Labels
Robust loss functions are essential for training accurate deep neural ne...
research
∙
04/13/2020
