17 citations · 46 across the 15 of their papers we have counts for
9 papers · 1 filter
On the Generalization Properties of Learning the Random Feature Models with Learnable Activation Functions
Zailin Ma, Jiansheng Yang, Yaodong Yang
This paper studies the generalization properties of a recently proposed kernel method, the Random Feature models with Learnable Activation Functions (RFLAF). By applying a data-dep…
Learning Expressive Random Feature Models via Parametrized Activations
Zailin Ma, Jiansheng Yang, Yaodong Yang
The random feature (RF) method is a powerful kernel approximation technique, but it typically uses fixed activation functions, limiting its adaptability across diverse tasks. To ov…
Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective
Yifei Wang, Liangchen Li, Jiansheng Yang +2
Adversarial Training (AT) has become arguably the state-of-the-art algorithm for extracting robust features. However, researchers recently notice that AT suffers from severe robust…
A Message Passing Perspective on Learning Dynamics of Contrastive Learning
Yifei Wang, Qi Zhang, Tianqi Du +3
In recent years, contrastive learning achieves impressive results on self-supervised visual representation learning, but there still lacks a rigorous understanding of its learning…
Optimization-Induced Graph Implicit Nonlinear Diffusion
Qi Chen, Yifei Wang, Yisen Wang +2
Due to the over-smoothing issue, most existing graph neural networks can only capture limited dependencies with their inherently finite aggregation layers. To overcome this limitat…
A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training
Yifei Wang, Yisen Wang, Jiansheng Yang +1
Adversarial Training (AT) is known as an effective approach to enhance the robustness of deep neural networks. Recently researchers notice that robust models with AT have good gene…
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap
Yifei Wang, Qi Zhang, Yisen Wang +2
Recently, contrastive learning has risen to be a promising approach for large-scale self-supervised learning. However, theoretical understanding of how it works is still unclear. I…
Residual Relaxation for Multi-view Representation Learning
Yifei Wang, Zhengyang Geng, Feng Jiang +4
Multi-view methods learn representations by aligning multiple views of the same image and their performance largely depends on the choice of data augmentation. In this paper, we no…
Dissecting the Diffusion Process in Linear Graph Convolutional Networks
Yifei Wang, Yisen Wang, Jiansheng Yang +1
Graph Convolutional Networks (GCNs) have attracted more and more attentions in recent years. A typical GCN layer consists of a linear feature propagation step and a nonlinear trans…