Pinned Repositories
Book-Mathmatical-Foundation-of-Reinforcement-Learning
This is the homepage of a new book entitled "Mathmatical Foundations of Reinforcement Learning."
D-GEX
Deep learning for gene expression inference
deeplearning-biology
A list of deep learning implementations in biology
deepvariant
DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.
DLforGenomics
Review Paper: Deep Learning for Genomics: A Concise Overview
GCNG
using graph convolutional neural network and spaital transcriptomics data to infer cell-cell interactions
GNNPapers
Must-read papers on graph neural networks (GNN)
Graph-Representation-Learning-Tutorial
Code for Data61's tutorial on Graph Representation Learning
graphSAGE-pytorch
A PyTorch implementation of GraphSAGE. This package contains a PyTorch implementation of GraphSAGE.
pinns-torch
PINNs-Torch, Physics-informed Neural Networks (PINNs) implemented in PyTorch.
Hongjinwu's Repositories
Hongjinwu/pinns-torch
PINNs-Torch, Physics-informed Neural Networks (PINNs) implemented in PyTorch.
Hongjinwu/Book-Mathmatical-Foundation-of-Reinforcement-Learning
This is the homepage of a new book entitled "Mathmatical Foundations of Reinforcement Learning."
Hongjinwu/D-GEX
Deep learning for gene expression inference
Hongjinwu/deeplearning-biology
A list of deep learning implementations in biology
Hongjinwu/deepvariant
DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.
Hongjinwu/DLforGenomics
Review Paper: Deep Learning for Genomics: A Concise Overview
Hongjinwu/GCNG
using graph convolutional neural network and spaital transcriptomics data to infer cell-cell interactions
Hongjinwu/GNNPapers
Must-read papers on graph neural networks (GNN)
Hongjinwu/Graph-Representation-Learning-Tutorial
Code for Data61's tutorial on Graph Representation Learning
Hongjinwu/graphSAGE-pytorch
A PyTorch implementation of GraphSAGE. This package contains a PyTorch implementation of GraphSAGE.
Hongjinwu/graphsage-simple
Simple reference implementation of GraphSAGE.
Hongjinwu/hello-world
test
Hongjinwu/molecular-VAE
Implementation of the paper - Automatic chemical design using a data-driven continuous representation of molecules
Hongjinwu/neural-fingerprint
Convolutional nets which can take molecular graphs of arbitrary size as input.
Hongjinwu/NGS-analysis
二代测序数据分析
Hongjinwu/ORGAN
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
Hongjinwu/papers_for_protein_design_using_DL
List of papers about Proteins Design using Deep Learning
Hongjinwu/PINNpapers
Must-read Papers on Physics-Informed Neural Networks.
Hongjinwu/progen
Official release of the ProGen models
Hongjinwu/protein-bert-pytorch
Implementation of ProteinBERT in Pytorch
Hongjinwu/pytorch-handbook
pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行
Hongjinwu/REINVENT
Molecular De Novo design using Recurrent Neural Networks and Reinforcement Learning
Hongjinwu/ShareBooks
ShareBooks
Hongjinwu/SPRINT_gan
Privacy-preserving generative deep neural networks support clinical data sharing
Hongjinwu/wae
Wasserstein Auto-Encoders
Hongjinwu/WassersteinGAN