GaryBall's Stars
steveazzolin/gdl_tutorial_turinginst
Material for the hands-on tutorial on Graph Deep Learning held at the Alan Turing Institute
Jhy1993/Datasets-for-Heterogeneous-Graph
Datasets-for-Heterogeneous-Graph
xpuoxford/L2G-neurips2021
oxnsg/seminar
yihong-chen/ReFactorGNN
Implementation for ReFactor GNNs
epignatelli/helx
Interoperating between (Deep) Reiforcement Learning libraries
jingyuexing/Ebook
📚 各类图书
KarolisMart/AgentNet
Reference implementation for Agent-based Graph Neural Networks
BDBC-KG-NLP/QA-Survey-CN
北京航空航天大学大数据高精尖中心自然语言处理研究团队开展了智能问答的研究与应用总结。包括基于知识图谱的问答(KBQA),基于文本的问答系统(TextQA),基于表格的问答系统(TableQA)、基于视觉的问答系统(VisualQA)和机器阅读理解(MRC)等,每类任务分别对学术界和工业界进行了相关总结。
huggingface/transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Davidham3/ASTGCN-2019-mxnet
⚠️[Deprecated] no longer maintained, please use the code in https://github.com/guoshnBJTU/ASTGCN-r-pytorch
panzheyi/ST-MetaNet
The codes and data of paper "Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning"
Yunbo426/predrnn-pp
Code release for "PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning" (ICML 2018)