Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural Networks.
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Updated
May 22, 2024 - Jupyter Notebook
Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural Networks.
Compute Lyapunov exponents and Covariant-Lyapunov-Vectors of an RNN update trajectory
Continuous regular group convolutions for Pytorch
Implementation of DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
ImageNet1k-pretrained SE(2) Equivariant Vision Models
DiffLinker: Equivariant 3D-Conditional Diffusion Model for Molecular Linker Design
[NeurIPS'23 Spotlight] Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance (LPS), in PyTorch
Library to make any existing neural network architecture equivariant
[NAACL 2022] Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning.
Implementation of Group-Convolutions as Keras layers.
A curated collection of resources and research related to the geometry of representations in the brain, deep networks, and beyond
Implementation of Torsional Diffusion for Molecular Conformer Generation (NeurIPS 2022)
Official source code for "Latent Field Discovery in Interacting Dynamical Systems with Neural Fields". In NeurIPS 2023.
Official PyTorch Implementation of "A General Framework for Robust G-Invariance in G-Equivariant Networks," NeurIPS 2023
Code repository of the paper "Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series, TMLR" https://arxiv.org/abs/2006.05259
Implementation of the Equiformer, SE3/E3 equivariant attention network that reaches new SOTA, and adopted for use by EquiFold for protein folding
Implementation of E(n)-Transformer, which incorporates attention mechanisms into Welling's E(n)-Equivariant Graph Neural Network
Geom3D: Geometric Modeling on 3D Structures, NeurIPS 2023
The Transformational Measures (TM) library allows neural network researchers to evaluate the invariance and equivariance of their models with respect to a set of transformations. Support for Pytorch (current) and Tensorflow/Keras (coming).
Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/
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