Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Graph Neural Network Library for PyTorch

PyTorch implementations of deep reinforcement learning algorithms and environments

Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

Code and hyperparameters for the paper "Generative Adversarial Networks"

FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs o…

Deep recommender models using PyTorch.

An open-source framework for machine learning and other computations on decentralized data.

Library for training machine learning models with privacy for training data

PyTorch for Semantic Segmentation

A PyTorch Implementation of Federated Learning

This is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks'

Semi-supervised learning with graph embeddings

Leaf: A Benchmark for Federated Settings

Neural Graph Collaborative Filtering, SIGIR2019

A collection of Google research projects related to Federated Learning and Federated Analytics.

RSTutorials: A Curated List of Algorithms about Traditional and Social Recommender System.

Federated Optimization in Heterogeneous Networks (MLSys '20)

code and docs for the EMNLP paper "DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning"

Simulate a federated setting and run differentially private federated learning.

Graph Neural Networks for Social Recommendation, WWW'19

Image segmentation - general superpixel segmentation & center detection & region growing

Efficient implementation of Generative Stochastic Networks

Code implementation of the paper "Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks", at IEEE Security and Privacy 2019.

Source code for paper "How to Backdoor Federated Learning" (https://arxiv.org/abs/1807.00459)

Fair Resource Allocation in Federated Learning (ICLR '20)

[ICLR 2018] Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

Implementation of the paper "Adversarial Attacks on Neural Networks for Graph Data".