24 citations · 35 across the 9 of their papers we have counts for
5 papers · 1 filter
The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks
Ziquan Liu, Yufei Cui, Yan Yan +4
In safety-critical applications such as medical imaging and autonomous driving, where decisions have profound implications for patient health and road safety, it is imperative to m…
Boosting Adversarial Robustness From The Perspective of Effective Margin Regularization
Ziquan Liu, Antoni B. Chan
The adversarial vulnerability of deep neural networks (DNNs) has been actively investigated in the past several years. This paper investigates the scale-variant property of cross-e…
Weight Rescaling: Effective and Robust Regularization for Deep Neural Networks with Batch Normalization
Ziquan Liu, Yufei Cui, Jia Wan +2
Weight decay is often used to ensure good generalization in the training practice of deep neural networks with batch normalization (BN-DNNs), where some convolution layers are inva…
Variational Nested Dropout
Yufei Cui, Yu Mao, Ziquan Liu +5
Nested dropout is a variant of dropout operation that is able to order network parameters or features based on the pre-defined importance during training. It has been explored for:…
Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations
Ziquan Liu, Yufei Cui, Antoni B. Chan
Using weight decay to penalize the L2 norms of weights in neural networks has been a standard training practice to regularize the complexity of networks. In this paper, we show tha…