Interactive exploration of equivariant neural networks on homogeneous spaces, with a focus on the sphere S² as SO(3)/SO(2). From Lecture 8 of the Lie groups course with Quantum Formalism

OpenEquivariance: a fast, open-source GPU JIT kernel generator for the Clebsch-Gordon Tensor Product.

A Local Frame-based Atomistic Potential

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).

Lorentz-equivariant Transformer based on Lorentz Local Canonicalization (Spinner et al. 2025)

Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural Networks (ICML 2023)

Learning generalizable shape completion with SIM(3) equivariance [NeurIPS 2025]

[ICCV 2025 Highlight] ETCH: Generalizing Body Fitting to Clothed Humans via Equivariant Tightness

Multi-domain Distribution Learning for De Novo Drug Design

Implementation of SE3-Transformers for Equivariant Self-Attention, in Pytorch. This specific repository is geared towards integration with eventual Alphafold2 replication.

Randomization Tests for Conditional Group Symmetry

Steerable convolutions

Practical Equivariances via Relational Conditional Neural Processes (Huang et al., NeurIPS 2023)

[ICML'25] The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products

Exploring the design surface of ACE for crystal deep learning

A Euclidean diffusion model for structure-based drug design.

Implementation of Group-Convolutions as Keras layers.

A convolution-equivariant deep-learning approach to segmentation of histopathology data

Clifford-Steerable Convolutional Neural Networks [ICML'24]

Implementation of DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking