22 citations · 47 across the 35 of their papers we have counts for
9 papers · 1 filter
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…
On the Hölder Stability of Multiset and Graph Neural Networks
Yair Davidson, Nadav Dym
Extensive research efforts have been put into characterizing and constructing maximally separating multiset and graph neural networks. However, recent empirical evidence suggests t…
Fourier Sliced-Wasserstein Embedding for Multisets and Measures
Tal Amir, Nadav Dym
We present the Fourier Sliced-Wasserstein (FSW) embedding - a novel method to embed multisets and measures over R^d into Euclidean space. Our proposed embedding approximately prese…
A transversality theorem for semi-algebraic sets with application to signal recovery from the second moment and cryo-EM
Tamir Bendory, Nadav Dym, Dan Edidin +1
Semi-algebraic priors are ubiquitous in signal processing and machine learning. Prevalent examples include a) linear models where the signal lies in a low-dimensional subspace; b)…