Python package built to ease deep learning on graph, on top of existing DL frameworks.
Python package for graph neural networks in chemistry and biology
The Clinical Trials Knowledge Graph
Visualization tool for Graph Neural Networks
A very simple framework for state-of-the-art Natural Language Processing (NLP)
Repository for rstWeb, a browser based annotation interface for Rhetorical Structure Theory
This repository is the implementation of "Top-down RST Parsing Utilizing Granularity Levels in Documents" published at AAAI 2020.
Preprocessing code and BERT/XLNet baselines for PDTB 2.0 and 3.0
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
Moleculenet.ai Datasets And Splits
code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503
Fast Discourse Parser to find latent Rhetorical STructure (RST) in text.
Platform for designing and evaluating Graph Neural Networks (GNN)
Protein Graph Library
KDD CUP 2020 Automatic Graph Representation Learning: 1st Place Solution
Website for DGL project
A knowledge graph and a set of tools for drug repurposing
Strategies for Pre-training Graph Neural Networks
Docker container for running PyTorch scripts to train and host PyTorch models on SageMaker
An open-source benchmark for graph machine learning
A tensorflow implementation of GCN-LPA
Template-free prediction of organic reaction outcomes
Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker