Xiaoyingzi09

Xiaoyingzi09's Stars

• Xiaoyingzi09/note-book

  2

• pcottle/learnGitBranching

  An interactive git visualization and tutorial. Aspiring students of git can use this app to educate and challenge themselves towards mastery of git!

  Language:JavaScript30.4k5.7k

• krahets/hello-algo

  《Hello 算法》：动画图解、一键运行的数据结构与算法教程。支持 Python, Java, C++, C, C#, JS, Go, Swift, Rust, Ruby, Kotlin, TS, Dart 代码。简体版和繁体版同步更新，English version ongoing

  Language:Java95.4k12.1k

• sheldonresearch/ProG

  A Unified Python Library for Graph Prompting

  Language:Python48958

• JHL-HUST/NAGphormer

  NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs

  Language:Python10614

• binary-husky/gpt_academic

  为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, moss等。

  Language:Python64k7.9k

• qitianwu/DIFFormer

  The official implementation for ICLR23 spotlight paper "DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion"

  Language:Python29132

• yongqyu/MolGAN-pytorch

  Pytroch implementation of MolGAN: An implicit generative model for small molecular graphs (https://arxiv.org/abs/1805.11973)

  Language:Python16142

• kamikaze0923/jtvae

  Junctional Tree Variational Auto-encoder

  Language:Python173

• qitianwu/NodeFormer

  The official implementation of NeurIPS22 spotlight paper "NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification"

  Language:Jupyter Notebook28627

• SNU-ARC/Ginex

  Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching

  Language:Python348

• stefan-ainetter/grasp_det_seg_cnn

  Code for ICRA21 paper "End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB".

  Language:Python13317

• Jhy1993/HAN

  Heterogeneous Graph Neural Network

  Language:Python1.1k220

• facebookresearch/shaDow_GNN

  NeurIPS 2021: Improve the GNN expressivity and scalability by decoupling the depth and receptive field of state-of-the-art GNN architectures

  Language:Python13316

• zhoushengisnoob/HAHE

  Code for Arxiv 2018 Paper "HAHE: Hierarchical Attentive Heterogeneous Information Network Embedding"

  Language:Python316

• librahu/HERec

  Source code for TKDE 2018 "Heterogeneous information network embedding for recommendation"

  Language:TeX16461

• Arnaud15/CS236_Neural_Processes_For_Image_Completion

  Language:Python84

• dragen1860/MAML-Pytorch

  Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)

  Language:Python2.3k421

• TachibanaYoshino/AnimeGAN

  A Tensorflow implementation of AnimeGAN for fast photo animation ! This is the Open source of the paper 「AnimeGAN: a novel lightweight GAN for photo animation」, which uses the GAN framwork to transform real-world photos into anime images.

  Language:Python4.5k659

• timesler/facenet-pytorch

  Pretrained Pytorch face detection (MTCNN) and facial recognition (InceptionResnet) models

  Language:Python4.4k945

• devnag/pytorch-generative-adversarial-networks

  A very simple generative adversarial network (GAN) in PyTorch

  Language:Python1.5k453

• zexihuang/random-walk-embedding

  A Broader Picture of Random-walk Based Graph Embedding

  Language:Python283

• zepx/graphencoder

  Learning Deep Representations for Graph Clustering Implementation

  Language:Python187

• zfjsail/gae-pytorch

  Graph Auto-Encoder in PyTorch

  Language:Python40977

• dongwei156/n2n

  This project involves the code and supplementary materials of paper "Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization".

  Language:Python30

• yzhang1918/kdd2020seal

  Codes and data for KDD2020 submission

  177

• TrustAGI-Lab/ARGA

  This is a TensorFlow implementation of the Adversarially Regularized Graph Autoencoder(ARGA) model as described in our paper: Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., & Zhang, C. (2018). Adversarially Regularized Graph Autoencoder for Graph Embedding, [https://www.ijcai.org/proceedings/2018/0362.pdf].

  Language:Python18660

• SeongJinAhn/VGNAE

  (CIKM'21) Variational Graph Normalized Auto-Encoders

  Language:Python338

• Tiger101010/DAEGC

  PyTorch implementation of Deep Attention Embedding Graph Clustering (19IJCAI) https://www.ijcai.org/Proceedings/2019/0509.pdf

  Language:Python10519

• lt610/GraphSaint

  The dgl implementation of GraphSaint

  Language:Python136