b'Haggai Maron'

research

∙ 04/06/2023

Hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching

Cross-device user matching is a critical problem in numerous domains, in...

0 Ali Taghibakhshi, et al. ∙

research

∙ 03/06/2023

Graph Positional Encoding via Random Feature Propagation

Two main families of node feature augmentation schemes have been explore...

0 Moshe Eliasof, et al. ∙

research

∙ 02/22/2023

Equivariant Polynomials for Graph Neural Networks

Graph Neural Networks (GNN) are inherently limited in their expressive p...

0 Omri Puny, et al. ∙

research

∙ 01/30/2023

Equivariant Architectures for Learning in Deep Weight Spaces

Designing machine learning architectures for processing neural networks ...

0 Aviv Navon, et al. ∙

research

∙ 10/28/2022

Generalized Laplacian Positional Encoding for Graph Representation Learning

Graph neural networks (GNNs) are the primary tool for processing graph-s...

0 Sohir Maskey, et al. ∙

research

∙ 06/22/2022

Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries

Subgraph GNNs are a recent class of expressive Graph Neural Networks (GN...

51 Fabrizio Frasca, et al. ∙

research

∙ 04/18/2022

Optimizing Tensor Network Contraction Using Reinforcement Learning

Quantum Computing (QC) stands to revolutionize computing, but is current...

0 Eli A. Meirom, et al. ∙

research

∙ 03/02/2022

A Simple and Universal Rotation Equivariant Point-cloud Network

Equivariance to permutations and rigid motions is an important inductive...

6 Ben Finkelshtein, et al. ∙

research

∙ 02/02/2022

Multi-Task Learning as a Bargaining Game

In Multi-task learning (MTL), a joint model is trained to simultaneously...

0 Aviv Navon, et al. ∙

research

∙ 01/20/2022

Federated Learning with Heterogeneous Architectures using Graph HyperNetworks

Standard Federated Learning (FL) techniques are limited to clients with ...

14 Or Litany, et al. ∙

research

∙ 12/18/2021

Weisfeiler and Leman go Machine Learning: The Story so far

In recent years, algorithms and neural architectures based on the Weisfe...

65 Christopher Morris, et al. ∙

research

∙ 10/06/2021

Equivariant Subgraph Aggregation Networks

Message-passing neural networks (MPNNs) are the leading architecture for...

8 Beatrice Bevilacqua, et al. ∙

research

∙ 08/02/2021

StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators

Can a generative model be trained to produce images from a specific doma...

13 Rinon Gal, et al. ∙

research

∙ 04/14/2021

Deep Permutation Equivariant Structure from Motion

Existing deep methods produce highly accurate 3D reconstructions in ster...

0 Dror Moran, et al. ∙

research

∙ 10/22/2020

Position-Agnostic Multi-Microphone Speech Dereverberation

Neural networks (NNs) have been widely applied in speech processing task...

0 Yochai Yemini, et al. ∙

research

∙ 10/17/2020

On Size Generalization in Graph Neural Networks

Graph neural networks (GNNs) can process graphs of different sizes but t...

0 Gilad Yehudai, et al. ∙

research

∙ 10/11/2020

How to Stop Epidemics: Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks

We consider the problem of monitoring and controlling a partially-observ...

0 Eli A. Meirom, et al. ∙

research

∙ 10/06/2020

On the Universality of Rotation Equivariant Point Cloud Networks

Learning functions on point clouds has applications in many fields, incl...

0 Nadav Dym, et al. ∙

research

∙ 03/29/2020

Self-Supervised Learning for Domain Adaptation on Point-Clouds

Self-supervised learning (SSL) allows to learn useful representations fr...

13 Idan Achituve, et al. ∙

research

∙ 03/12/2020

Learning Algebraic Multigrid Using Graph Neural Networks

Efficient numerical solvers for sparse linear systems are crucial in sci...

0 Ilay Luz, et al. ∙

research

∙ 02/20/2020

Set2Graph: Learning Graphs From Sets

Many problems in machine learning (ML) can be cast as learning functions...

28 Hadar Serviansky, et al. ∙

research

∙ 02/20/2020

On Learning Sets of Symmetric Elements

Learning from unordered sets is a fundamental learning setup, which is a...

21 Haggai Maron, et al. ∙

research

∙ 05/28/2019

Controlling Neural Level Sets

The level sets of neural networks represent fundamental properties such ...

0 Matan Atzmon, et al. ∙

research

∙ 05/27/2019

Provably Powerful Graph Networks

Recently, the Weisfeiler-Lehman (WL) graph isomorphism test was used to ...

0 Haggai Maron, et al. ∙

research

∙ 01/27/2019

On the Universality of Invariant Networks

Constraining linear layers in neural networks to respect symmetry transf...

0 Haggai Maron, et al. ∙

research

∙ 12/27/2018

Surface Networks via General Covers

Developing deep learning techniques for geometric data is an active and ...

6 Niv Haim, et al. ∙

research

∙ 12/24/2018

Invariant and Equivariant Graph Networks

Invariant and equivariant networks have been successfully used for learn...

12 Haggai Maron, et al. ∙

research

∙ 06/06/2018

Multi-chart Generative Surface Modeling

This paper introduces a 3D shape generative model based on deep neural n...

2 Heli Ben Hamu, et al. ∙

research

∙ 03/27/2018

Point Convolutional Neural Networks by Extension Operators

This paper presents Point Convolutional Neural Networks (PCNN): a novel ...

0 Matan Atzmon, et al. ∙

research

∙ 05/17/2017

DS++: A flexible, scalable and provably tight relaxation for matching problems

Correspondence problems are often modelled as quadratic optimization pro...

0 Nadav Dym, et al. ∙

Haggai Maron

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