Non-parametric hypothesis tests for identifying distributional group symmetries from data
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Updated
Aug 1, 2023 - Julia
Non-parametric hypothesis tests for identifying distributional group symmetries from data
Compute Lyapunov exponents and Covariant-Lyapunov-Vectors of an RNN update trajectory
Original code for "Exploiting Learned Symmetries in Group Equivariant Convolutions"
Code for SIGGRAPH paper CNNs on Surfaces using Rotation-Equivariant Features
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).
Implementation of VectorNeuron-Transformer paper
Image to Icosahedral Projection for SO(3) Object Reasoning from Single-View Images
Practical Equivariances via Relational Conditional Neural Processes (Huang et al., NeurIPS 2023)
Official repository for Color Equivariant Convolutional Networks.
Official source code for "Latent Field Discovery in Interacting Dynamical Systems with Neural Fields". In NeurIPS 2023.
ImageNet1k-pretrained SE(2) Equivariant Vision Models
Implementation of Group-Convolutions as Keras layers.
Experiments with Group Equivariant Convolutional Networks
A List of Papers on Theoretical Foundations of Graph Neural Networks
[ICML 2023 Oral] Official environments and implementations for "Subequivariant Graph Reinforcement Learning in 3D Environments"
A PyTorch port of EMLP JAX library (Finzi et al. 2021)
Official PyTorch Implementation of "A General Framework for Robust G-Invariance in G-Equivariant Networks," NeurIPS 2023
Authors' implementation of the paper "Equivariant Networks for Pixelized Spheres" published at ICML 2021.
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