Your PyTorch AI Factory - Flash enables you to easily configure and run complex AI recipes for over 15 tasks across 7 data domains

Physics-Informed Neural networks for Advanced modeling

Bag of Tricks for Graph Neural Networks.

Bags of Tricks in OGB (node classification) with GCNs.

GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.

Batched optimisation algorithms for neural network potential driven molecular dynamics.

Code repo for the paper "AIO-P: Expanding Neural Performance Predictors Beyond Image Classification", accepted to AAAI-23.

g2-MLP: State-of-the-Art Model for Node Classification on Graphs (PPI Dataset)

Code repo for the paper "GENNAPE: Towards Generalized Neural Architecture Performance Estimators", accepted to AAAI-23.

Project for Deep Learning And Applied AI course at the University of "La Sapienza" in Master in Computer Science A.A. 2021/2022

🧬 Official implementation of Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks [ICML 2024]

Graph Neural Networks: From Zero To Hero

This repository is a brief tutorial about how Graph convolutional networks and message passing networks work with example code demonstration using pytorch and torch_geometric

Here is an implementation of Title: "Are Graph Convolutional Networks With Random Weights Feasible?" Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence Year: 2023

Wide-model collective ensemble system with fractal, geometric, and heavy compilation optimizations.

This project focuses on predicting maternal-fetal health outcomes, specifically early detection of preeclampsia, by learning transferable representations from cfRNA and placental transcriptomic data.

In this repo I have implement different applications of GNN which covers Node Level, Edge level, Graph level tasks with different GNN variants such as GCN, GAT, Graph SAGE

CUA-GUI-Operator is an experimental, advanced computer-use agent (CUA) and visual action grounding suite designed to bridge the gap between natural language instructions and graphical user interface (GUI) interactions.

Melty Molecule is a molecular property prediction system that leverages graph neural networks to predict melting points of chemical compounds. The model uses a hybrid architecture combining Principal Neighborhood Aggregation (PNA) and Graph Isomorphism Networks (GIN) with edge updates and virtual node connectivity.

A neuroevolutionary framework for structural optimization that trains Graph Neural Networks (GNNs) with a genetic algorithm.