Hugging Face Trending Papers August 12, 2026

Reducing Symmetry Increase in Equivariant Neural Networks

Read the original on Hugging Face Trending Papers →

Equivariant Neural Networks (ENNs) have empowered numerous applications in scientific fields. Despite their remarkable capacity for representing geometric structures, ENNs suffer from degraded expressivity when processing symmetric inputs: the output representations are invariant to transformations that extend beyond the input's symmetries.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv Machine Learning

6d ago

Reducing Symmetry Increase in Equivariant Neural Networks

arXiv:2608. 12010v1 Announce Type: new Abstract: Equivariant Neural Networks (ENNs) have empowered numerous applications in scientific fields.

By Ning Lin, Jiacheng Cen, Anyi Li, Wenbing Huang, Hao Sun

arXiv AI

Jul 7

Foundations of Equivariant Deep Learning: Unifying Graph and Sheaf Neural Networks

arXiv:2607. 03798v1 Announce Type: cross Abstract: Symmetry is everywhere in nature and society.

By Yoshihiro Maruyama

arXiv Machine Learning

Jul 7

A Complete Symmetry Classification of Shallow ReLU Networks

arXiv:2604. 14037v2 Announce Type: replace Abstract: Parameter space is not function space for neural network architectures.

By Pranavkrishnan Ramakrishnan

arXiv Machine Learning

Aug 10

Equivariant Sparse Autoencoders: Mechanistic Interpretability of Neural Networks on Symmetric Data

arXiv:2511. 09432v2 Announce Type: replace Abstract: Machine learning (ML) models achieve remarkable performance but remain hard to interpret due to their scale and complexity.

By Ege Erdogan, Ana Lucic

safety

arXiv Machine Learning

Jun 4

Beyond Structural Symmetries: Linear Mode Connectivity via Neuron Identifiability

arXiv:2606. 04754v1 Announce Type: new Abstract: Many striking phenomena in deep learning, such as linear mode connectivity and the structured behavior of training dynamics, are closely tied to parameter symmetries: transformations that leave the realized function unchanged.

By Vincent B\"urgin, Daniel Herbst, Ya-Wei Eileen Lin, Stefanie Jegelka

safety

arXiv Machine Learning

Jun 15

Equivariant Representation Learning via Class-Pose Decomposition

arXiv:2207. 03116v4 Announce Type: replace Abstract: We introduce a general method for learning representations that are equivariant to symmetries of data.

By Giovanni Luca Marchetti, Gustaf Tegn\'er, Anastasiia Varava, Danica Kragic

diffusion

Related stories

Reducing Symmetry Increase in Equivariant Neural Networks

Foundations of Equivariant Deep Learning: Unifying Graph and Sheaf Neural Networks

A Complete Symmetry Classification of Shallow ReLU Networks

Equivariant Sparse Autoencoders: Mechanistic Interpretability of Neural Networks on Symmetric Data

Beyond Structural Symmetries: Linear Mode Connectivity via Neuron Identifiability

Equivariant Representation Learning via Class-Pose Decomposition