no code implementations • 24 Jul 2022 • Nikolaos Tsilivis, Jingtong Su, Julia Kempe
Adversarial training and its variants have come to be the prevailing methods to achieve adversarially robust classification using neural networks.
no code implementations • 29 Sep 2021 • Shiji Xin, Yifei Wang, Jingtong Su, Yisen Wang
Extensive experiments show that our proposed DAT can effectively remove the domain-varying features and improve OOD generalization on both correlation shift and diversity shift tasks.
1 code implementation • NeurIPS 2020 • Jingtong Su, Yihang Chen, Tianle Cai, Tianhao Wu, Ruiqi Gao, Li-Wei Wang, Jason D. Lee
In this paper, we conduct sanity checks for the above beliefs on several recent unstructured pruning methods and surprisingly find that: (1) A set of methods which aims to find good subnetworks of the randomly-initialized network (which we call "initial tickets"), hardly exploits any information from the training data; (2) For the pruned networks obtained by these methods, randomly changing the preserved weights in each layer, while keeping the total number of preserved weights unchanged per layer, does not affect the final performance.