185 citations · 297 across the 9 of their papers we have counts for
9 papers
Towards Understanding Fast Adversarial Training
Bai Li, Shiqi Wang, Suman Jana +1
Current neural-network-based classifiers are susceptible to adversarial examples. The most empirically successful approach to defending against such adversarial examples is adversa…
Towards Practical Lottery Ticket Hypothesis for Adversarial Training
Bai Li, Shiqi Wang, Yunhan Jia +4
Recent research has proposed the lottery ticket hypothesis, suggesting that for a deep neural network, there exist trainable sub-networks performing equally or better than the orig…
Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction
Yantao Lu, Yunhan Jia, Jianyu Wang +4
Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i.e., they remain adversarial even against other m…
Graph-Driven Generative Models for Heterogeneous Multi-Task Learning
Wenlin Wang, Hongteng Xu, Zhe Gan +6
We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework. The proposed model is based on the fact that heterogen…
On Norm-Agnostic Robustness of Adversarial Training
Bai Li, Changyou Chen, Wenlin Wang +1
Adversarial examples are carefully perturbed in-puts for fooling machine learning models. A well-acknowledged defense method against such examples is adversarial training, where ad…
Improving Sequence-to-Sequence Learning via Optimal Transport
Liqun Chen, Yizhe Zhang, Ruiyi Zhang +7
Sequence-to-sequence models are commonly trained via maximum likelihood estimation (MLE). However, standard MLE training considers a word-level objective, predicting the next word…