📝 Java 数据结构和算法 | Algorithms and data structures implemented in Java with explanations and links to further readings

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A library for graph deep learning research

Papers about explainability of GNNs

Officially Accepted to IEEE Transactions on Medical Imaging (TMI, IF: 11.037) - Special Issue on Geometric Deep Learning in Medical Imaging.

Code for TKDE paper "Self-supervised learning on graphs: Contrastive, generative, or predictive"

Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization

A curated list of papers in Test-time Adaptation, Test-time Training and Source-free Domain Adaptation

PyTorch implementation of Contrastive Learning methods

SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners

A comprehensive list of awesome contrastive self-supervised learning papers.

Collection of awesome test-time (domain/batch/instance) adaptation methods

[NeurIPS 2020] "Graph Contrastive Learning with Augmentations" by Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen

关于domain generalization，domain adaptation，causality，robutness，prompt，optimization，generative model各式各样研究的阅读笔记

links to conference publications in graph-based deep learning

The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"

[NeurIPS 2023] Does Invariant Graph Learning via Environment Augmentation Learn Invariance?

Time series explainability via self-supervised model behavior consistency

[ NeurIPS 2023 ] Official Codebase for "Conformal Meta-learners for Predictive Inference of Individual Treatment Effects"

code for progressive gsl

[NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.