CVPR2022-OrphicX

Graph Neural Network Library for PyTorch

Python package built to ease deep learning on graph, on top of existing DL frameworks.

Anomaly detection related books, papers, videos, and toolboxes

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

links to conference publications in graph-based deep learning

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

A unified, comprehensive and efficient recommendation library

SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)

? ? ? ? GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba | 一站式图计算系统

Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.