Official implementation of Field Convolutions for Surface CNNs [ICCV 2021 Oral]
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Updated
May 31, 2024 - Python
Official implementation of Field Convolutions for Surface CNNs [ICCV 2021 Oral]
Graph Neural Network Library for PyTorch
Contextualizing protein representations using deep learning on protein networks and single-cell data
设计一下怎么毕业
Library to make any existing neural network architecture equivariant
Protein Graph Library
Python Framework built on PyTorch and PyTorch Geometric for working with Representation Learning on Graph Neural Networks.
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/
gRNAde: Geometric Deep Learning for 3D RNA inverse design
Multi-language library for the calculation of spherical harmonics in Cartesian coordinates
Redes convolucionales definidas en grafos para la predicción de nuevas asociaciones gen-enfermedad
Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural Networks.
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
Low-Level Graph Neural Network Operators for PyG
PyNeuraLogic lets you use Python to create Differentiable Logic Programs
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