xiangwang1223

xiangwang1223's Stars

• toeverything/AFFiNE

  There can be more than Notion and Miro. AFFiNE(pronounced [ə‘fain]) is a next-gen knowledge base that brings planning, sorting and creating all together. Privacy first, open-source, customizable and ready to use.

  Language:TypeScript43.5k2302.3k2.8k

• huggingface/peft

  🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.

  Language:Python16.8k1111.1k1.7k

• microsoft/qlib

  Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

  Language:Python15.8k3019192.7k

• BradyFU/Awesome-Multimodal-Large-Language-Models

  :sparkles::sparkles:Latest Advances on Multimodal Large Language Models

  13.2k256128838

• voxel51/fiftyone

  Refine high-quality datasets and visual AI models

  Language:Python9k631.6k580

• OpenGVLab/LLaMA-Adapter

  [ICLR 2024] Fine-tuning LLaMA to follow Instructions within 1 Hour and 1.2M Parameters

  Language:Python5.8k79142376

• PharMolix/OpenBioMed

  Language:Python724115284

• LehengTHU/Agent4Rec

  [SIGIR 2024 perspective] The implementation of paper "On Generative Agents in Recommendation"

  Language:Python2923327

• MangoKiller/MolTC

  Language:Python22329442

• YangZhengyi98/DreamRec

  Language:Python10811013

• ljy0ustc/LLaRA

  Language:Python1031197

• tanyuqian/knowledge-harvest-from-lms

  ACL 2023 (Findings) - BertNet: Harvesting Knowledge Graphs from Pretrained Language Models

  Language:Python1026418

• acharkq/MolCA

  Code for EMNLP2023 paper "MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter".

  Language:Python831132

• lsh0520/3D-MoLM

  Language:Python412135

• junkangwu/ADNCE

  [NeurIPS2023] Official code of "Understanding Contrastive Learning via Distributionally Robust Optimization"

  Language:Python38111

• blacksingular/GHRN

  [WWW 2023] "Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum" by Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, Yongdong Zhang

  Language:Python35234

• syr-cn/SimSGT

  [NeurIPS 2023] "Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules"

  Language:Python32293

• YangZhengyi98/DROS

  Language:Python32104

• YangZhengyi98/RecInterpreter

  Language:Python23112

• anzhang314/BC-Loss

  Official code of "Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering" (2022 NeurIPS)

  Language:Python22262

• anzhang314/InvCF

  Official code of "Invariant Collaborative Filtering to Popularity Distribution Shift" (2023 WWW)

  Language:Jupyter Notebook20231

• blacksingular/wsdm_GDN

  [WSDM 2023] "Alleviating Structrual Distribution Shift in Graph Anomaly Detection" by Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, Yongdong Zhang

  Language:Python20210

• LehengTHU/AdvInfoNCE

  [NeurIPS 2023] The implementation of paper "Empowering Collaborative Filtering Generalization via Principled Adversarial Contrastive Loss"

  Language:Python18232

• syr-cn/ReLM

  [EMNLP 2023] ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction.

  Language:Python18110

• anzhang314/ReScore

  Official code of "Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting" (2023 ICLR)

  Language:Python16226

• yongduosui/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.

  Language:Python16110

• eltociear/MolCA

  Code for EMNLP2023 paper "MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter".

  Language:Python12104

• yl3800/EIGV

  Language:Python12140

• Arthurma71/AdvDrop

  Language:Python11111

• srhthu/LM-CompEval-Legal

  Code for the paper "A Comprehensive Evaluation of Large Language Models on Legal Judgment Prediction"

  Language:Python10121