ant2022tna

Generalization-Causality

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

LECI

The implementation of "Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization" (NeurIPS 2023)

AIA

[NeurIPS 2023] "Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift" by Yongduo Sui, Qitian Wu, Jiancan Wu, Qing Cui, Longfei Li, Jun Zhou, Xiang Wang, Xiangnan He.

RGCL

Ratioanle-aware Graph Contrastive Learning codebase

Awesome-Causality-Inspired-GNNs

Graph-OSDA

This is the code of our paper "Open-Set Graph Domain Adaptation via Separate Domain Alignment".

StableGNN

StableGNN-Generalizing Graph Neural Networks on Out-Of-Distribution Graphs

Shift-Robust-GNNs

"Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')

ICML2021-Gem

Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."

size-invariant-GNNs

Size-Invariant Graph Representations for Graph Classification Extrapolations (ICML 2021 Long Talk)

IRMBed

This is the project for IRM methods

downkyi

哔哩下载姬downkyi，哔哩哔哩网站视频下载工具，支持批量下载，支持8K、HDR、杜比视界，提供工具箱（音视频提取、去水印等）。

transferlearning

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

Kernelized-HRM

The code for our NeurIPS 2021 paper "Kernelized Heterogeneous Risk Minimization".

CIGA

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

Awesome-Pruning

A curated list of neural network pruning resources.

block_movement_pruning

Block Sparse movement pruning

BiP

[NeurIPS22] "Advancing Model Pruning via Bi-level Optimization" by Yihua Zhang*, Yuguang Yao*, Parikshit Ram, Pu Zhao, Tianlong Chen, Mingyi Hong, Yanzhi Wang, and Sijia Liu

PAIR

[ICLR 2023, ICLR DG oral] PAIR, the optimizer and model selection criteria for OOD Generalization

TempBalance

[NeurIPS 2023 Spotlight] Temperature Balancing, Layer-wise Weight Analysis, and Neural Network Training

pytorch_optimizer

optimizer & lr scheduler & loss function collections in PyTorch

pytorch-sso

PyTorch-SSO: Scalable Second-Order methods in PyTorch

dadaptation

D-Adaptation for SGD, Adam and AdaGrad

DIDA

Implementation Codes for NeurIPS22 paper "Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift"

GraphOOD-EERM

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

awesome-optimizers

Neural Network optimizers implemented from scratch in numpy (Adam, Adadelta, RMSProp, SGD, etc.)

GSAT

[ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.

AdaM3

[ICDM 2023] Momentum is All You Need for Data-Driven Adaptive Optimization

Best-Deep-Learning-Optimizers

Collection of the latest, greatest, deep learning optimizers (for Pytorch) - CNN, NLP suitable

prodigy

The Prodigy optimizer and its variants for training neural networks.