22 citations · 38 across the 11 of their papers we have counts for
11 papers · 1 filter
Transport, Don't Generate: Deterministic Geometric Flows for Combinatorial Optimization
Benjy Friedmann, Nadav Dym
Recent advances in Neural Combinatorial Optimization (NCO) have been dominated by diffusion models that treat the Euclidean Traveling Salesman Problem (TSP) as a stochastic $N \tim…
Quantitative Approximation Rates for Group Equivariant Learning
Jonathan W. Siegel, Snir Hordan, Hannah Lawrence +2
The universal approximation theorem establishes that neural networks can approximate any continuous function on a compact set. Later works in approximation theory provide quantitat…
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…
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…
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…