3 citations · 3 across the 4 of their papers we have counts for
5 papers · 1 filter
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 into Euclidean space. Our proposed embedding approxim…
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…
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…
Weisfeiler Leman for Euclidean Equivariant Machine Learning
Snir Hordan, Tal Amir, Nadav Dym
The -Weisfeiler-Leman (-WL) graph isomorphism test hierarchy is a common method for assessing the expressive power of graph neural networks (GNNs). Recently, GNNs whose expre…
Neural Injective Functions for Multisets, Measures and Graphs via a Finite Witness Theorem
Tal Amir, Steven J. Gortler, Ilai Avni +2
Injective multiset functions have a key role in the theoretical study of machine learning on multisets and graphs. Yet, there remains a gap between the provably injective multiset…