Anomaly detection related books, papers, videos, and toolboxes

Sequence modeling benchmarks and temporal convolutional networks

Implementation of Graph Auto-Encoders in TensorFlow

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

Trustworthy AI related projects

TensorFlow implementations of Graph Neural Networks

Invariant Information Clustering for Unsupervised Image Classification and Segmentation

Codebase for the paper LSTM Fully Convolutional Networks for Time Series Classification

Neural relational inference for interacting systems - pytorch

Implementation code for the paper "Graph Neural Network-Based Anomaly Detection in Multivariate Time Series" (AAAI 2021)

Code for the CIKM 2019 paper "DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting".

Code for the paper: Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data

Reimplementation of Graph Embedding methods by Pytorch.

PyTorch Implementation of "Towards K-Means-Friendly Spaces: Simultaneous Deep Learning and Clustering," Bo Yang et al., ICML'2017.

Variational Graph Recurrent Neural Networks - PyTorch

This is outdated -- New version: https://github.com/boyangumn/DCN-New

The paper "Learning Representations for Time Series Clustering"

Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"

This repository captures source code and data sets for our paper at the Causal Discovery & Causality-Inspired Machine Learning Workshop at Neural Information Processing Systems (NeurIPS) 2020.

A framework for generating complex and realistic datasets for use in evaluating causal inference methods.

The Mackey-Glass Anomaly Benchmark