22 citations · 47 across the 35 of their papers we have counts for
9 papers · 1 filter
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…
Quantitative Bounds for Sorting-Based Permutation-Invariant Embeddings
Nadav Dym, Matthias Wellershoff, Efstratios Tsoukanis +2
We study permutation-invariant embeddings of -dimensional point sets, which are defined by sorting independent one-dimensional projections of the input. Such embeddings aris…
Spectral Graph Neural Networks are Incomplete on Graphs with a Simple Spectrum
Snir Hordan, Maya Bechler-Speicher, Gur Lifshitz +1
Spectral features are widely incorporated within Graph Neural Networks (GNNs) to improve their expressive power, or their ability to distinguish among non-isomorphic graphs. One po…
On the (Non) Injectivity of Piecewise Linear Janossy Pooling
Ilai Reshef, Nadav Dym
Multiset functions, which are functions that map multisets to vectors, are a fundamental tool in the construction of neural networks for multisets and graphs. To guarantee that the…