Fieldlh

Fieldlh's Stars

• HELL-TO-HEAVEN/KGE-CL

  2

• Lee-zix/RE-GCN

  This is the official code release of the following paper: Zixuan Li, Xiaolong Jin, Wei Li, Saiping Guan, Jiafeng Guo, Huawei Shen, Yuanzhuo Wang and Xueqi Cheng. Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning

  Language:Python12923

• TemporalKGTeam/xERTE

  Language:Python4517

• claws-lab/jodie

  A PyTorch implementation of ACM SIGKDD 2019 paper "Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks"

  Language:Python34882

• twitter-research/tgn

  TGN: Temporal Graph Networks

  Language:Python942189

• code-ball/dynamic-embedding

  Language:Python51

• hongyuanmei/neurawkes

  Source code of The Neural Hawkes Process (NIPS 2017)

  Language:Python21050

• CunchaoZ/CyGNet

  Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks

  Language:Python9924

• SXKDZ/awesome-self-supervised-learning-for-graphs

  A curated list for awesome self-supervised learning for graphs.

  36845

• Jhy1993/Awesome-GNN-Recommendation

  Graph Neural Network

  26446

• weihua916/powerful-gnns

  How Powerful are Graph Neural Networks?

  Language:Python1.2k222

• malllabiisc/CompGCN

  ICLR 2020: Composition-Based Multi-Relational Graph Convolutional Networks

  Language:Python595106

• guillaumejaume/edGNN

  Code for the paper: "edGNN: A simple and powerful GNN for labeled graphs"

  Language:Python4310

• JiapengWu/TeMP

  Temporal Message Passing Network for Temporal Knowledge Graph Completion

  Language:Python5518

• Separius/awesome-fast-attention

  list of efficient attention modules

  Language:Python986109

• Jeff20100601/GHNN_clean

  Language:Python306

• SimiaoZuo/Transformer-Hawkes-Process

  Code for Transformer Hawkes Process, ICML 2020.

  Language:Python17448

• fastai/fastbook

  The fastai book, published as Jupyter Notebooks

  Language:Jupyter Notebook21.5k8.3k

• DA-southampton/Tech_Aarticle

  主要是我是日常看过的不错的文章的资源汇总，方便自己也分享给大家。有些我看过的，就会做简单的解读，没看过的，就先罗列一下，然后之后看了把解读更新上；涉及到搜索/推荐/自然语言处理。

  1.7k325

• mrdbourke/machine-learning-roadmap

  A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.

  7.5k1.2k

• nosuggest/Reflection_Summary

  算法理论基础知识应知应会

  2.5k510

• Machine-Learning-Tokyo/Math_resources

  34145

• GoogleCloudPlatform/ml-design-patterns

  Source code accompanying O'Reilly book: Machine Learning Design Patterns

  Language:Jupyter Notebook1.9k527

• kmario23/deep-learning-drizzle

  Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

  Language:HTML12k2.9k

• DeepGraphLearning/KnowledgeGraphEmbedding

  Language:Python1.2k265

• wds-seu/Knowledge-Graph-Publications

  Top-level Conference Publications on Knowledge Graph

  34070

• chris-chris/ml-engineer-roadmap

  WIP: Roadmap to becoming a machine learning engineer in 2020

  2.1k255

• INK-USC/RE-Net

  Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs (EMNLP 2020)

  Language:Python43393

• woojeongjin/dynamic-KG

  Dynamic (Temporal) Knowledge Graph Completion (Reasoning)

  587111

• paperswithcode/releasing-research-code

  Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)

  2.6k713