arXiv:2609.39078v1 Announce Type: new
Abstract: Representations are routinely used across machine learning, psychology, and neuroscience to draw inferences about the computations of biological and ar...
By Marvin Theiss, Lukas Braun, Andrew M. Saxe, Erin Grant
arXiv:2604. 14037v2 Announce Type: replace Abstract: Parameter space is not function space for neural network architectures.
By Pranavkrishnan Ramakrishnan
arXiv:2606. 10913v1 Announce Type: new Abstract: We explore whether intrinsic symmetries of the training data lead to conserved quantities during gradient-flow training of neural networks.
By Jakob Galley, Vahid Shahverdi, Axel Flinth
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.
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
Artificial neural networks generate local symmetries called fibrations and coverings during learning, and these covering symmetries are stable attractors of stochastic gradient descent. The study shows that such symmetries appear across diverse architectures—multilayer, convolutional, recurrent, and transformer networks—and can be exploited for drastic model compression, reducing networks to 17% of their original size without performance loss. Controlled breaking of covering symmetry further improves continual learning, achieving state‑of‑the‑art results.
By Osvaldo M Velarde, Lucas C Parra, Alireza Hashemi, Hernan A Makse