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Training PyTorch models with differential privacy
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Code for the paper "Adversarially Regularized Autoencoders (ICML 2018)" by Zhao, Kim, Zhang, Rush and LeCun
Generating Natural Adversarial Examples, ICLR 2018
Universal Adversarial Triggers for Attacking and Analyzing NLP (EMNLP 2019)
TextGAN is a PyTorch framework for Generative Adversarial Networks (GANs) based text generation models.
🏋️ Python / Modern C++ Solutions of All 4013 LeetCode Problems (Weekly Update)
Must-read Papers on Textual Adversarial Attack and Defense
Rethinking the Value of Network Pruning (Pytorch) (ICLR 2019)
PyTorch implementations of Generative Adversarial Networks.
Keras implementations of Generative Adversarial Networks.
Code for reproducing experiments in "Improved Training of Wasserstein GANs"
Pytorch implementation of convolutional neural network visualization techniques
Github Pages template based upon HTML and Markdown for personal, portfolio-based websites.
Privacy Engineering Collaboration Space
PyTorch implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation
A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Library for training machine learning models with privacy for training data
Perform data science on data that remains in someone else's server
An Open Source Machine Learning Framework for Everyone
LeetCode Solutions: A Record of My Problem Solving Journey.( leetcode题解，记录自己的leetcode解题之路。)