290 citations · 1.1k across the 23 of their papers we have counts for
8 papers · 1 filter
Modifying Memories in Transformer Models
Chen Zhu, Ankit Singh Rawat, Manzil Zaheer +4
Large Transformer models have achieved impressive performance in many natural language tasks. In particular, Transformer based language models have been shown to have great capabil…
Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks
Huimin Zeng, Chen Zhu, Tom Goldstein +1
Adversarial Training is proved to be an efficient method to defend against adversarial examples, being one of the few defenses that withstand strong attacks. However, traditional d…
Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer
Chen Zhu, Zheng Xu, Ali Shafahi +3
When large scale training data is available, one can obtain compact and accurate networks to be deployed in resource-constrained environments effectively through quantization and p…
Robust Optimization as Data Augmentation for Large-scale Graphs
Kezhi Kong, Guohao Li, Mucong Ding +5
Data augmentation helps neural networks generalize better by enlarging the training set, but it remains an open question how to effectively augment graph data to enhance the perfor…
Large-Scale Adversarial Training for Vision-and-Language Representation Learning
Zhe Gan, Yen-Chun Chen, Linjie Li +3
We present VILLA, the first known effort on large-scale adversarial training for vision-and-language (V+L) representation learning. VILLA consists of two training stages: (i) task-…
Headless Horseman: Adversarial Attacks on Transfer Learning Models
Ahmed Abdelkader, Michael J. Curry, Liam Fowl +5
Transfer learning facilitates the training of task-specific classifiers using pre-trained models as feature extractors. We present a family of transferable adversarial attacks agai…
Certified Defenses for Adversarial Patches
Ping-Yeh Chiang, Renkun Ni, Ahmed Abdelkader +3
Adversarial patch attacks are among one of the most practical threat models against real-world computer vision systems. This paper studies certified and empirical defenses against…
Improving the Tightness of Convex Relaxation Bounds for Training Certifiably Robust Classifiers
Chen Zhu, Renkun Ni, Ping-yeh Chiang +3
Convex relaxations are effective for training and certifying neural networks against norm-bounded adversarial attacks, but they leave a large gap between certifiable and empirical…