Ian Chen's starred repositories
Multi-Task-Learning-PyTorch
PyTorch implementation of multi-task learning architectures, incl. MTI-Net (ECCV2020).
ModelPoisoning
Code for "Analyzing Federated Learning through an Adversarial Lens" https://arxiv.org/abs/1811.12470
backdoor_federated_learning
Source code for paper "How to Backdoor Federated Learning" (https://arxiv.org/abs/1807.00459)
awesome-grad-school
🎓 Advice and resources for thriving and surviving graduate school
How-to-PhD
A collection of resources and information for concrete skills that are helpful when pursuing a PhD in computer science (specifically in ML/AI or related disciplines)
poisoning-benchmark
A unified benchmark problem for data poisoning attacks
geoparsepy
geoparsepy is a Python geoparsing library that will extract and disambiguate locations from text. It uses a local OpenStreetMap database which allows very high and unlimited geoparsing throughput, unlike approaches that use a third-party geocoding service (e.g. Google Geocoding API). this repository holds Python examples to use the PyPI library.
deep-learning-box
Build a deep learning box and setup software
Extensive_Evaluations
Experiments evaluating 10+ ml models in anomaly detection cases for continuous and implicit authentication using the HMOG data set.
Paper-with-Code-of-Wireless-communication-Based-on-DL
无线与深度学习结合的论文代码整理/Paper-with-Code-of-Wireless-communication-Based-on-DL
location-guard
Hide your geographic location from websites.
Input-Specific-Certification
The official implementation of AAAI22 paper: Input-Specific Robustness Certification for Randomized Smoothing
membership-inference
Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)
usercall.hpp
MSVC Visual C++ Preprocessor macros for custom calling conventions on functions
nn_robust_attacks
Robust evasion attacks against neural network to find adversarial examples
Security-and-Robustness-of-Deep-Learning-in-Wireless-Communication-Systems
A research oriented repository on the Security and Robustness of Deep Learning for Wireless Communication Systems