Graph Neural Network Library for PyTorch
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Updated
Jun 5, 2024 - Python
Graph Neural Network Library for PyTorch
Python Framework built on PyTorch and PyTorch Geometric for working with Representation Learning on Graph Neural Networks.
PyNeuraLogic lets you use Python to create Differentiable Logic Programs
Official implementation of Field Convolutions for Surface CNNs [ICCV 2021 Oral]
Contextualizing protein representations using deep learning on protein networks and single-cell data
设计一下怎么毕业
A library for differentiable robotics.
Package for the data-driven representation of non-linear dynamics over manifolds based on a statistical distribution of local phase portrait features. Includes specific example on dynamical systems, synthetic- and real neural datasets. https://agosztolai.github.io/MARBLE/
Redes convolucionales definidas en grafos para la predicción de nuevas asociaciones gen-enfermedad
Target-aware Variational Auto-encoders for Ligand Generation with Multimodal Protein Representation Learning
A novel architecture and training strategy for graph neural networks (GNN). The proposed architecture, named as Autoencoder-Aided GNN (AA-GNN), compresses the convolutional features at multiple hidden layers, hinging on a novel end-to-end training procedure that learns different graph representations per each layer. As a result, the computationa…
Continuous regular group convolutions for Pytorch
Triangle mesh deep learning utility library.
Pytorch Implementation of Group-equivariant Convolutional Networks
A scalable graph learning toolkit for extremely large graph datasets. (WWW'22, 🏆 Best Student Paper Award)
[ICLR'24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Implementation of PocketGen: Generating Full-Atom Ligand-Binding Protein Pockets
Code for our Nature Scientific Reports paper "A universal framework for accurate and efficient geometric deep learning of molecular systems"
[ICML 2024] LSH-Based Efficient Point Transformer (HEPT)
Geometric deep learning of protein–DNA binding specificity
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