2 papers
cs.LG2026
Permutation-Equivariant Flow Matching for Alignment-Free Neural Weight Generation
Arkadi Piven, Yam Eitan, Guy Bar-Shalom +5
A trained neural network can be represented by a parameter vector in high dimensions. Learning distributions over these vectors enables the generation of new models across various…
cs.CV2025
Learning Eigenstructures of Unstructured Data Manifolds
Roy Velich, Arkadi Piven, David Bensaïd +3
We introduce a novel framework that directly learns a spectral basis for shape and manifold analysis from unstructured data, eliminating the need for traditional operator selection…