Yisen Wang

On the Convergence and Robustness of Adversarial Training

2021 · arXiv (Cornell University)

Improving the robustness of deep neural networks (DNNs) to adversarial examples is an important yet challenging problem for secure deep learning. Across existing defense techniques, adversarial training with Projected Gradient Decent (PGD) is amongst the …

Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality

2018 · Own your potential (DEAKIN)

© Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved. Deep Neural Networks (DNNs) have recently been shown to be vulnerable against adversarial examples, which are carefully crafted instances that can mislead DNNs to …

Dissecting the Diffusion Process in Linear Graph Convolutional Networks

2021 · arXiv (Cornell University)

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 transformation step. Recent works show that a linear …

Robust Long-Tailed Learning via Label-Aware Bounded CVaR

2023 · arXiv (Cornell University)

Data in the real-world classification problems are always imbalanced or long-tailed, wherein the majority classes have the most of the samples that dominate the model training. In such setting, the naive model tends to have …