====
论文
====

======
Python
=====
=

=========
Tensorflow
=========

=======
PyTorch
=======

=====
Keras
=====

====
专题
====

====
链接
====

====
视频

====

=======
药物设计

=======

=======
材料科学
=======

============
经济学与金融学
==========
==


GitHub资源

PyTorch code Tensorflow code Bioinformatics 论文代码 GNN code 教程  

=============
图神经网络代码
==========
===

/awesome-gcn
This repository is to collect GCN, GAT(graph attention) related resources.

/3DGNN_pytorch:
This is the Pytorch implementation of 3D Graph Neural Networks for RGBD Semantic Segmentation.

benedekrozemberczki/CapsGNN:
PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019).

graphdeeplearning/benchmarking-gnns:
Repository for benchmarking graph neural networks

svjan5/GNNs-for-NLP:
Tutorial: Graph Neural Networks for Natural Language Processing at EMNLP 2019 and CODS-COMAD 2020.

XinyiZ001/CapsGNN
ensorflow implementation of Capsule Graph Neural Network

yazdotai/graph-networks
A list of interesting graph neural networks (GNN) links with a primary interest in recommendations and tensorflow tha…

/pytorch_geometric
Must-read papers on GNN
Graph Convolutional Networks
This is a TensorFlow implementation of Graph Convolutional Networks for the task of (semi-supervised) classification of nodes in a graph.
 
商汤科技的GNN_PPI模型:https://github.com/lvguofeng/GNN_PPI
Codes and models for the paper "Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction".
 
GraphDTA: prediction of drug–target binding affinity using graph convolutional networks (pdf) GitHub: GraphDTA
 
GRAPH ATTENTION NETWORKS (pdf) || GitGub code
 
GraphSAGE: Inductive Representation Learning on Large Graphs (web) GitHub code: GraphSAGE
 
 
Identifying drug–target interactions based on graph convolutional network and deep neural network
Geometric deep learning on graphs and manifolds using mixture model CNNs (pdf)
WideDTA: prediction of drug-target binding affinity (pdf)
 
 
 

人工智能

人工智能(Artificial Intelligence),英文缩写为AI。它是研究、开发用于模拟、延伸和扩展人的智能的理论、方法、技术及应用系统的一门新的技术科学。 人工智能是计算机科学的一个分支,它企图了解智能的实质,并生产出一种新的能以人类智能相似的方式做出反应的智能机器,该领域的研究包括机器人、语言识别、图像识别、自然语言处理和专家系统等。人工智能从诞生以来,理论和技术日益成熟,应用领域也不断扩大,可以设想,未来人工智能带来的科技产品,将会是人类智慧的“容器”。人工智能可以对人的意识、思维的信息过程的模拟。人工智能不是人的智能,但能像人那样思考、也可能超过人的智能。

上海市浦东新区沪城环路999号