111 citations · 734 across the 50 of their papers we have counts for
11 papers · 1 filter
TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization
Ziquan Liu, Yi Xu, Xiangyang Ji +1
Recent years have seen the ever-increasing importance of pre-trained models and their downstream training in deep learning research and applications. At the same time, the defense…
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…
Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios
Xueying Zhan, Zeyu Dai, Qingzhong Wang +4
Pool-based Active Learning (AL) has achieved great success in minimizing labeling cost by sequentially selecting informative unlabeled samples from a large unlabeled data pool and…
A Comparative Survey of Deep Active Learning
Xueying Zhan, Qingzhong Wang, Kuan-hao Huang +3
While deep learning (DL) is data-hungry and usually relies on extensive labeled data to deliver good performance, Active Learning (AL) reduces labeling costs by selecting a small p…
Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes
Xueying Zhan, Qing Li, Antoni B. Chan
Active learning aims to achieve greater accuracy with less training data by selecting the most useful data samples from which it learns. Single-criterion based methods (i.e., infor…
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:…
ALdataset: a benchmark for pool-based active learning
Xueying Zhan, Antoni Bert Chan
Active learning (AL) is a subfield of machine learning (ML) in which a learning algorithm could achieve good accuracy with less training samples by interactively querying a user/or…
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…
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over Simplex
Yufei Cui, Wuguannan Yao, Qiao Li +2
Estimating the predictive uncertainty of a Bayesian learning model is critical in various decision-making problems, e.g., reinforcement learning, detecting adversarial attack, self…
Clustering hidden Markov models with variational HEM
Emanuele Coviello, Antoni B. Chan, Gert R. G. Lanckriet
The hidden Markov model (HMM) is a widely-used generative model that copes with sequential data, assuming that each observation is conditioned on the state of a hidden Markov chain…