24 citations · 61 across the 6 of their papers we have counts for
4 papers · 1 filter
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…