Pinned Repositories
CIGA
[NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
FeAT
[NeurIPS 2023] Understanding and Improving Feature Learning for Out-of-Distribution Generalization
GALA
[NeurIPS 2023] Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
GIA-HAO
[ICLR 2022] Understanding and Improving Graph Injection Attack by Promoting Unnoticeability
GMT
[ICML 2024] How Interpretable Are Interpretable Graph Neural Networks?
GOOD
[SOTA results in GOOD benchmark🚀] CIGA Implementation under GOOD Benchamrk
Learning_CS224N
My approach to CS224n [AT] Stanford 2019Winter -- Natural Language Processing with Deep Learning. 💡
Learning_CS224w
My approach to CS224w [AT] Stanford 2019 : )
PAIR
[ICLR 2023, ICLR DG oral] PAIR, the optimizer and model selection criteria for OOD Generalization
Research_Navigation
Recordings of my research navigation, including paper/book reading notes and related implementations
LFhase's Repositories
LFhase/Learning_CS224w
My approach to CS224w [AT] Stanford 2019 : )
LFhase/CIGA
[NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
LFhase/PAIR
[ICLR 2023, ICLR DG oral] PAIR, the optimizer and model selection criteria for OOD Generalization
LFhase/GIA-HAO
[ICLR 2022] Understanding and Improving Graph Injection Attack by Promoting Unnoticeability
LFhase/FeAT
[NeurIPS 2023] Understanding and Improving Feature Learning for Out-of-Distribution Generalization
LFhase/GALA
[NeurIPS 2023] Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
LFhase/Learning_CS224N
My approach to CS224n [AT] Stanford 2019Winter -- Natural Language Processing with Deep Learning. 💡
LFhase/GOOD
[SOTA results in GOOD benchmark🚀] CIGA Implementation under GOOD Benchamrk
LFhase/GMT
[ICML 2024] How Interpretable Are Interpretable Graph Neural Networks?
LFhase/LFhase.github.io
personal website
LFhase/DI-star
OpenDILab Decision AI in StarCraftII
LFhase/awesome-causality-algorithms
An index of algorithms for learning causality with data
LFhase/2019-scalingattack
Image-Scaling Attacks and Defenses
LFhase/awesome-DrugAI
Research repo for AI aided drug discovery, de novo drug development and related topics
LFhase/awesome-graph-ood
Papers about out-of-distribution generalization on graphs.
LFhase/DomainBed
DomainBed is a suite to test domain generalization algorithms
LFhase/DrugOOD
OOD Dataset Curator and Benchmark for AI-aided Drug Discovery
LFhase/fish
Official implementation of paper Gradient Matching for Domain Generalization
LFhase/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
LFhase/graph-adversarial-learning-literature
A curated list of adversarial attacks and defenses papers on graph-structured data.
LFhase/interpretable-ml-book
Book about interpretable machine learning
LFhase/InterpretEval
Interpretable Evaluation for (Almost) All NLP Tasks
LFhase/InvarianceUnitTests
Toy datasets to evaluate algorithms for domain generalization and invariance learning.
LFhase/LFhase
LFhase/ott
LFhase/pytorch-styleguide
An unofficial styleguide and best practices summary for PyTorch
LFhase/Recognizers-Text
Microsoft.Recognizers.Text provides recognition and resolution of numbers, units, and date/time expressed in multiple languages (ZH, EN, FR, ES, PT, DE, IT, TR, HI. Partial support for NL, JA, KO, SV). Contributions are greatly welcome! Packages are available at https://www.nuget.org/profiles/Recognizers.Text and https://www.npmjs.com/~recognizers.text
LFhase/StarCraft
Implementations of QMIX, VDN, COMA, QTRAN, MAVEN, CommNet, DyMA-CL, and G2ANet on SMAC, the decentralised micromanagement scenario of StarCraft II
LFhase/wikitextprocessor
Python package for WikiMedia dump processing (Wiktionary, Wikipedia etc). Wikitext parsing, template expansion, Lua module execution. For data extraction, bulk syntax checking, error detection, and offline formatting.
LFhase/Workarounds-for-ARM-mac
This repository describes how I get most of my configurations work on the new Apple Silicon Mac