Continuous regular group convolutions for Pytorch
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Updated
May 23, 2024 - Python
Continuous regular group convolutions for Pytorch
Matlab implementation of Trajectory Invariants.
Rust newtype with guarantees 🇺🇦 🦀
Measurement invariance explorer - R package to explore measurement invariance
Simple examples to illustrate the differences between invariance, covariance and contravariance in Scala
Official PyTorch and JAX Implementation of "Harmonics of Learning: Universal Fourier Features Emerge in Invariant Networks"
Official PyTorch Implementation of "A General Framework for Robust G-Invariance in G-Equivariant Networks," NeurIPS 2023
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).
Have hard time understanding it? Let me simplify it for you.
Code for "Improving Stain Invariance of CNNs for Segmentation by Fusing Channel Attention and Domain-Adversarial Training"
A List of Papers on Theoretical Foundations of Graph Neural Networks
Non-parametric hypothesis tests for identifying distributional group symmetries from data
On the forward invariance of Neural ODEs: performance guarantees for policy learning
Equivariant Subgraph Aggregation Networks (ICLR 2022 Spotlight)
This repository is the official accompaniment to A General Framework for Robust G-Invariance in G-Equivariant Networks (submitted, NeurIPS 2023)
Relative representations can be leveraged to enable solving tasks regarding "latent communication": from zero-shot model stitching to latent space comparison between diverse settings.
This a tensorflow implementation of VICReg - a self-supervised learning architecture that prevents collapse in an intuitive manner using a loss function that 1. maintains the variance of each embedding over a batch above a threshold and 2. decorrelating pairs of embeddings over a batch and attracting them to 0. Training was done using TPU on colab
MMD-B-Fair: Learning Fair Representations with Statistical Testing (AISTATS 2023)
Official PyTorch implementation of Bispectral Neural Networks, ICLR 2023
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