cywuuuu

cywuuuu's Stars

• mrdbourke/pytorch-deep-learning

  Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.

  Language:Jupyter Notebook13.1k1272113.7k

• juewuy/ShellCrash

  Run sing-box/mihomo as client in shell

  Language:Shell10k617521.6k

• wookayin/tensorflow-talk-debugging

  💬 Slides and supplementary codes for my talk 'Debugging Tips on TensorFlow' (2016)

  Language:Python611406145

• Coder-Yu/SELFRec

  An open-source framework for self-supervised recommender systems.

  Language:Python56677180

• chrischoy/pytorch-custom-cuda-tutorial

  Tutorial for building a custom CUDA function for Pytorch

  Language:Python51213451

• datawhalechina/torch-rechub

  A Lighting Pytorch Framework for Recommendation Models, Easy-to-use and Easy-to-extend.

  Language:Python48492680

• HKUDS/LLMRec

  [WSDM'2024 Oral] "LLMRec: Large Language Models with Graph Augmentation for Recommendation"

  Language:Python43832655

• awslabs/graphstorm

  Enterprise graph machine learning framework for billion-scale graphs for ML scientists and data scientists.

  Language:Python4011129361

• VocabVictor/clash-for-AutoDL

  AutoDL平台服务器适配梯子， 使用 Clash 作为代理工具

  Language:Shell23504527

• George-Miao/clashctl

  CLI for interacting with clash

  Language:Rust19122814

• HKUDS/LightGCL

  [ICLR'2023] "LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation"

  Language:Python18352717

• wushangbin/MGFN

  MGFN: Multi-Graph Fusion Networks for Urban Region Embedding (MGFN, https://fanxlxmu.github.io/publication/ijcai22/) was accepted by IJCAI-2022.

  Language:Python18316227

• RUCAIBox/UniSRec

  [KDD'22] Official PyTorch implementation for "Towards Universal Sequence Representation Learning for Recommender Systems".

  Language:Python18142326

• reczoo/RecZoo

  A curated model zoo for recommendation tasks

  Language:Python17913745

• RUCAIBox/VQ-Rec

  [WWW'23] PyTorch implementation for "Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders".

  Language:Python712511

• YuxueYang1204/CudaDemo

  Implement custom operators in PyTorch with cuda/c++

  Language:Python562110

• soyan1999/ccf-csp-python

  ccf考试历届真题python语言解答（持续更新中）

  Language:Python493111

• liufancs/IMP_GCN

  Language:Python43199

• qwerfdsaplking/SATrans

  The source code for our paper "Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction" (accepted by KDD2023 Applied Science Track), which proposes a model for Multi-Scenario/Multi-Domain Recommendation.

  Language:Python34234

• LoadingByte/are-gnn-defenses-robust

  Adaptive evaluation reveals that most examined adversarial defenses for GNNs show no or only marginal improvement in robustness. (NeurIPS 2022)

  Language:Jupyter Notebook29344

• weiyinwei/GRCN

  Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback

  Language:Python28116

• archersama/Uni-CTR

  Source code of TOIS paper "A Unified Framework for Multi-Domain CTR Prediction via Large Language Models"

  Language:Python26201

• xhcgit/LightGCN-implicit-DGL

  Language:Python25213

• DavidZWZ/LightGODE

  [CIKM 2024] Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation

  Language:Python12112

• likuanppd/GOOD-AT

  The code of ICLR 2024 paper: Boosting the Adversarial Robustness of Graph Neural Networks: An OOD Perspective

  Language:Python10210

• xurong-liang/LEGCF

  Language:Python5100

• zhengyk11/SRJGraph

  This is the codes for the paper "Joint Learning of E-commerce Search and Recommendation with a Unified Graph Neural Network" published in WSDM 2022.

  Language:Python5200

• JQHang/Paths2Pair

  Language:Python4110

• tanatosuu/sgfcf

  KDD 2024: How Powerful is Graph Filtering for Recommendation

  Language:Python3112

• LittlePierre/open3dLines

  Simple 3D linear CAD

  Language:Python120