This project is has a generator network that is able to produce human-like handwritten numbers and faces without ever seeing any of them.
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Updated
Dec 15, 2018 - HTML
This project is has a generator network that is able to produce human-like handwritten numbers and faces without ever seeing any of them.
Code to run a novel recurrent-GNN model for weather prediction. Data: https://www.kaggle.com/PROPPG-PPG/hourly-weather-surface-brazil-southeast-region
The PyTorch 1.6 and Python 3.7 implementation for the paper Graph Convolutional Networks for Text Classification
GraSeq: Graph and Sequence Fusion Learning for Molecular Property Prediction. In CIKM 2020.
In this project I explore an potential approach to estimate a human’s intention in a dyadic collaborative manipulation task by learning to predict the intended future trajectory of the co-manipulated object via the latent graph representation of the system.
An implementation of R-GCN model detection on IEEE-Fraud Detection Dataset,
Dense and Sparse Implementation of GAT written by PyTorch
GOPS: A Machine Learning Framework for Opioid Abuse Analysis
The official implementation of Convergent Graph Solvers (CGS)
A collection of social datasets for RecBole-GNN.
Implementation of FedGNN (Federated GNN) using pyG library
This paper explores the idea of using heterogeneous graph neural networks (Het-GNN) to partition old legacy monoliths into candidate microservices. We additionally take membership constraints that come from a subject matter expert who has deep domain knowledge of the application.
Clustering Hi-C contact map using graph neural networks. Utilities and data pipelines. Created as part of Bioinformatics institute spring 2022 project
CS224W: Graph Embedding, GNNs, Recommendation Systems, and applications.
GNN News Fake Detection model with implementation of GCN Convolution layer
Learning to Count Isomorphisms with Graph Neural Networks
A project emulating a GNN model which uses EEG data to identify depression in individuals.
The repository is a collection of Jupyter notebooks showcasing various projects related to graph neural networks (GNNs). Each notebook provides a detailed explanation of the project and its implementation, making it easy for users to understand and replicate the results.
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