98 citations · 622 across the 36 of their papers we have counts for
6 papers · 1 filter
Generalized Laplacian Positional Encoding for Graph Representation Learning
Sohir Maskey, Ali Parviz, Maximilian Thiessen +3
Graph neural networks (GNNs) are the primary tool for processing graph-structured data. Unfortunately, the most commonly used GNNs, called Message Passing Neural Networks (MPNNs) s…
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries
Fabrizio Frasca, Beatrice Bevilacqua, Michael M. Bronstein +1
Subgraph GNNs are a recent class of expressive Graph Neural Networks (GNNs) which model graphs as collections of subgraphs. So far, the design space of possible Subgraph GNN archit…
Optimizing Tensor Network Contraction Using Reinforcement Learning
Eli A. Meirom, Haggai Maron, Shie Mannor +1
Quantum Computing (QC) stands to revolutionize computing, but is currently still limited. To develop and test quantum algorithms today, quantum circuits are often simulated on clas…
A Simple and Universal Rotation Equivariant Point-cloud Network
Ben Finkelshtein, Chaim Baskin, Haggai Maron +1
Equivariance to permutations and rigid motions is an important inductive bias for various 3D learning problems. Recently it has been shown that the equivariant Tensor Field Network…
Sign and Basis Invariant Networks for Spectral Graph Representation Learning
Derek Lim, Joshua Robinson, Lingxiao Zhao +4
We introduce SignNet and BasisNet -- new neural architectures that are invariant to two key symmetries displayed by eigenvectors: (i) sign flips, since if is an eigenvector the…
Federated Learning with Heterogeneous Architectures using Graph HyperNetworks
Or Litany, Haggai Maron, David Acuna +3
Standard Federated Learning (FL) techniques are limited to clients with identical network architectures. This restricts potential use-cases like cross-platform training or inter-or…