22 citations · 62 across the 7 of their papers we have counts for
18 papers
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…
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…
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…
Deep Permutation Equivariant Structure from Motion
Dror Moran, Hodaya Koslowsky, Yoni Kasten +3
Existing deep methods produce highly accurate 3D reconstructions in stereo and multiview stereo settings, i.e., when cameras are both internally and externally calibrated. Neverthe…
On the Universality of Rotation Equivariant Point Cloud Networks
Nadav Dym, Haggai Maron
Learning functions on point clouds has applications in many fields, including computer vision, computer graphics, physics, and chemistry. Recently, there has been a growing interes…