Learning 3D ML
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Updated
Nov 20, 2021 - Jupyter Notebook
Learning 3D ML
Thesis project repository
Repository of the paper "CoreGDM: Geometric Deep Learning Network Decycling and Dismantling" by M. Grassia and G. Mangioni
A deep neural network with hybrid architecture (EGNN + Transformer) for molecular properties prediction.
Official Implementation of Graph Mixer Networks
Virality prediction of Twitter hashtags via Graph Neural Networks.
This is the code of the paper Breaking the Expressive Bottleneck of Graph Neural Networks.
📚 A fork of PointNet++ for a study on geometric deep learning
Here there are some useful stuff I have written to work on topological data analysis. Some of the code was developed with specific datasets in mind, but most should generalize well.
Exploring QSAR Models for Activity-Cliff Prediction
Torch implementation of Marc Finzi's Equivariant MLP
DockGame: Cooperative Games for Multimeric Rigid Protein Docking
Python Framework built on PyTorch and PyTorch Geometric for working with Representation Learning on Graph Neural Networks.
Code for Self-Supervised Few-Shot Learning on Point Clouds paper at NeurIPS 2020
MongeNet sampler official implementation
Designing amino acids around hotspots
Git repo for the paper "Physics-constrained predictive molecular latent space discovery with graph scattering variational autoencoder."
The GMols server providing functionalities to the GMols client.
PyTorch implementation of "DeepSphere: a Graph-based Spherical CNN", Defferard et al., 2019.
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