an Equivariant Neural Network?

[2205.07362] What is an equivariant neural network? - arXiv

We explain equivariant neural networks, a notion underlying breakthroughs in machine learning from deep convolutional neural networks for computer vision.

Equivariant neural networks - what, why and how? | Maurice Weiler

This post is the first in a series on equivariant deep learning and coordinate independent CNNs. The goal of the current post is to give a first introduction ...

an Equivariant Neural Network? - American Mathematical Society

an Equivariant Neural. Network? Lek-Heng Lim and Bradley J. Nelson. We explain equivariant neural networks, a notion under- lying ...

10. Equivariant Neural Networks

Equivariant neural networks guarantee equivariance by construction for arbitrary groups, which removes the need to align trajectories, work in special ...

E(3)-equivariant graph neural networks for data-efficient ... - Nature

This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic ...

Equivariant Neural Networks | Part 1/3 - Introduction - YouTube

Papers / Resources ▭▭▭ Fabian Fuchs Equivariance: https://fabianfuchsml.github.io/equivariance1of2/ Deep Learning for Molecules: ...

Chen-Cai-OSU/awesome-equivariant-network - GitHub

Paper list for equivariant neural network. Contribute to Chen-Cai-OSU/awesome-equivariant-network development by creating an account on GitHub.

Geometric deep learning and equivariant neural networks

A gauge equivariant network for such fields consists of layers which are equivariant with respect to change of coordinates in \mathcal {M}, such ...

Equivariant neural networks and piecewise linear representation ...

Equivariant neural networks are neural networks with symmetry. Motivated by the theory of group representations, we decompose the layers of an equivariant ...

General framework for E(3)-equivariant neural network ... - Nature

The equivariant building blocks of the neural network are implemented using the scheme provided by Tensor-Field Networks and e3nn. The feature ...

Theory for Equivariant Quantum Neural Networks

A comprehensive theoretical framework to design equivariant quantum neural networks (EQNNs) for essentially any relevant symmetry group.

An equivariant graph neural network for the elasticity tensors of all ...

Here, we report the materials tensor (MatTen) model for rapid and accurate prediction of the full fourth-rank elasticity tensors of crystals.

[D] Benefits of Equivariant Networks : r/MachineLearning - Reddit

Equivariant NNs are useful if you know that your optimal solution must be equivariant to a specific transformation of the input. In this case, ...

UvA - An Introduction to Group Equivariant Deep Learning | uvagedl

UvA - An Introduction to Group Equivariant Deep Learning. Welcome to the public page for the mini-course on Group Equivariant Deep Learning. The course is still ...

Naturally Occurring Equivariance in Neural Networks - Distill.pub

The equivariant behavior we observe in neurons is really a reflection of a deeper symmetry that exists in the weights of neural networks and the ...

Equivariant Neural Networks | Part 3/3 - Transformers and GNNs

Papers / Resources ▭▭▭ SchNet: https://arxiv.org/abs/1706.08566 SE(3) Transformer: https://arxiv.org/abs/2006.10503 Tensor Field Network: ...

DavidRuhe/clifford-group-equivariant-neural-networks - GitHub

Code used by the "Clifford Group Equivariant Neural Networks" paper. - DavidRuhe/clifford-group-equivariant-neural-networks.

Any-dimensional equivariant neural networks

Third, is there a user-friendly. Page 2. Eitan Levin, Mateo Díaz procedure for learning these networks? We proceed to tackle these challenges. Free equivariant ...

Equivariant Neural Network - Easiio

Equivariant Neural Networks (ENNs) are designed to maintain symmetry and invariance properties in their outputs relative to transformations applied to their ...

Equivariant Graph Neural Networks for Toxicity Prediction

Our results show that ET adequately learns 3D representations of molecules that can successfully correlate with toxicity activity, achieving good accuracies.