Enjoy the magic of Diffusion models!

Causal discovery algorithms and tools for implementing new ones

DAGs with NO TEARS: Continuous Optimization for Structure Learning

Causal Discovery in Python. It also includes (conditional) independence tests and score functions.

Reimplementation of NOTEARS in Tensorflow

Official PyTorch implementation of Learning to (Learn at Test Time): RNNs with Expressive Hidden States

Graph Attention Networks (https://arxiv.org/abs/1710.10903)

GFlowNet library specialized for graph & molecular data

An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)

Honor of Kings AI Open Environment of Tencent

Code to reproduce experiments in paper: "Amortized Variational Inference: When and Why?"

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

Best Practices on Recommendation Systems

Official implementation of SIGIR'2021 paper: "Sequential Recommendation with Graph Neural Networks".

This is our Tensorflow implementation for "Graph-based Embedding Smoothing for Sequential Recommendation" (GES) TKDE 2021.

LeetCode Solutions: A Record of My Problem Solving Journey.( leetcode题解，记录自己的leetcode解题之路。)

📚 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计

A diffusion-based framework for spatio-temporal point processes

A framework for large scale recommendation algorithms.

COIN-OR Branch-and-Cut solver

Methods and Implements of Deep Clustering

A Python-embedded modeling language for convex optimization problems.

This is our implementation of IntEL-Intent-aware Ranking Ensemble for Personalized Recommendation (SIGIR2023)

NeuralProphet: A simple forecasting package

A collection of resources and papers on Diffusion Models

A list for dynamic inference research, including: dynamic routing, anytime inference and conditional computation

Survival analysis with PyTorch

A review and evaluation on CTR prediction models