63 citations · 92 across the 6 of their papers we have counts for
6 papers
Rethink the Connections among Generalization, Memorization and the Spectral Bias of DNNs
Xiao Zhang, Haoyi Xiong, Dongrui Wu
Over-parameterized deep neural networks (DNNs) with sufficient capacity to memorize random noise can achieve excellent generalization performance, challenging the bias-variance tra…
Tiny noise, big mistakes: Adversarial perturbations induce errors in Brain-Computer Interface spellers
Xiao Zhang, Dongrui Wu, Lieyun Ding +4
An electroencephalogram (EEG) based brain-computer interface (BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled indi…
Empirical Studies on the Properties of Linear Regions in Deep Neural Networks
Xiao Zhang, Dongrui Wu
A deep neural network (DNN) with piecewise linear activations can partition the input space into numerous small linear regions, where different linear functions are fitted. It is b…
Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs
Zihan Liu, Lubin Meng, Xiao Zhang +2
Multiple convolutional neural network (CNN) classifiers have been proposed for electroencephalogram (EEG) based brain-computer interfaces (BCIs). However, CNN models have been foun…
Active Learning for Black-Box Adversarial Attacks in EEG-Based Brain-Computer Interfaces
Xue Jiang, Xiao Zhang, Dongrui Wu
Deep learning has made significant breakthroughs in many fields, including electroencephalogram (EEG) based brain-computer interfaces (BCIs). However, deep learning models are vuln…
On the Vulnerability of CNN Classifiers in EEG-Based BCIs
Xiao Zhang, Dongrui Wu
Deep learning has been successfully used in numerous applications because of its outstanding performance and the ability to avoid manual feature engineering. One such application i…