Xiao Zhang's repositories
EEGAdversary
This is a toolbox to construct adversarial examples of EEG signals. The traditional EEG extraction methods and classifiers are re-implemented in Tensorflow.
Speller-Attacks
This project attacked widely-used EEG spellers, e.g., P300 spellers and SSVEP spellers, with adversarial perturbation templates. We constructed these templates to perform target attacks on EEG spellers, demonstrating that the spellers can be manipulated to output anything the attacker wants when added the templates.
Lottery-Ticket-Hypothesis-for-DNNs
This repo aims to provide an easy-to-use interface for searching the lottery ticket of a DNN structure.
KerasAdversary
This project is aimed to provide a simple interface to attack the models built with Keras. It can be very simple to craft adversarial examples with different attacking methods using our API.
LFP-Analyze
This project is mainly about Local Field Potential (LFP).
OptimizationVariance
This repository is the official implementation of "Optimization Variance: Delve into the Epoch-Wise Double Descent of DNNs"
ZhangXiao96.github.io
https://zhangxiao96.github.io/
CNNZoo4CIFAR
This is a collection of CNNs trained on CIFAR10, e.g. DenseNet, VGG16, ResNet...
advertorch
A Toolbox for Adversarial Robustness Research
arl-eegmodels
This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow
LeetcodeTop
汇总各大互联网公司容易考察的高频leetcode题🔥
pytorch-cnn-visualizations
Pytorch implementation of convolutional neural network visualization techniques
pytorch_influence_functions
This is a PyTorch reimplementation of Influence Functions from the ICML2017 best paper: Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang.
RecommenderSystems4Python
This is a respository which implements several recomender systems in Python.
RethinkSpectralBias
This repository is the official implementation of "Rethink the Connections among Generalization, Memorization and Spectral Bias" https://arxiv.org/abs/2004.13954.
daxigua
最简单的魔改发布『 合成大西瓜 』，配套改图工具，不用改代码，修改配置即可！
exploring-generalization
An comparative study on the paper Exploring Generalization in Deep Learning https://papers.nips.cc/paper/7176-exploring-generalization-in-deep-learning.pdf
generalization-bounds
Computing various measures and generalization bounds on convolutional and fully connected networks
PyTorch-VAE
A Collection of Variational Autoencoders (VAE) in PyTorch.