9 papers
Finite Probes Suffice: Identifiability and Universality for Weight-Space Learning
Soutrik Sarangi, Yonatan Sverdlov, Adir Dayan +2
Learning properties of neural networks has recently attracted growing interest, with existing approaches operating either directly on network parameters or through probe-based repr…
When and How to Canonize: A Generalization Perspective
Yonatan Sverdlov, Benjamin Friedman, Snir Hordan +1
While invariant architectures are standard for processing symmetric data, there is growing interest in achieving invariance by applying group averaging or canonization to non-invar…
Short-Range Oversquashing
Yaaqov Mishayev, Yonatan Sverdlov, Tal Amir +1
Message Passing Neural Networks (MPNNs) are widely used for learning on graphs, but their ability to process long-range information is limited by the phenomenon of oversquashing. T…
Toward bilipshiz geometric models
Yonatan Sverdlov, Eitan Rosen, Nadav Dym
Many neural networks for point clouds are, by design, invariant to the symmetries of this datatype: permutations and rigid motions. The purpose of this paper is to examine whether…
Monotone and Separable Set Functions: Characterizations and Neural Models
Soutrik Sarangi, Yonatan Sverdlov, Nadav Dym +1
Motivated by applications for set containment problems, we consider the following fundamental problem: can we design set-to-vector functions so that the natural partial order on se…
FSW-GNN: A Bi-Lipschitz WL-Equivalent Graph Neural Network
Yonatan Sverdlov, Yair Davidson, Nadav Dym +1
Famously, the ability of Message Passing Neural Networks (MPNN) to distinguish between graphs is limited to graphs separable by the Weisfeiler-Lemann (WL) graph isomorphism test, a…
Revisiting Multi-Permutation Equivariance through the Lens of Irreducible Representations
Yonatan Sverdlov, Ido Springer, Nadav Dym
This paper explores the characterization of equivariant linear layers for representations of permutations and related groups. Unlike traditional approaches, which address these pro…
On the Expressive Power of Sparse Geometric MPNNs
Yonatan Sverdlov, Nadav Dym
Motivated by applications in chemistry and other sciences, we study the expressive power of message-passing neural networks for geometric graphs, whose node features correspond to…
Efficient Rehearsal Free Zero Forgetting Continual Learning using Adaptive Weight Modulation
Yonatan Sverdlov, Shimon Ullman
Artificial neural networks encounter a notable challenge known as continual learning, which involves acquiring knowledge of multiple tasks over an extended period. This challenge a…