98 citations · 622 across the 36 of their papers we have counts for
10 papers · 1 filter
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…
Scene-Agnostic Multi-Microphone Speech Dereverberation
Yochai Yemini, Ethan Fetaya, Haggai Maron +1
Neural networks (NNs) have been widely applied in speech processing tasks, and, in particular, those employing microphone arrays. Nevertheless, most existing NN architectures can o…
From Local Structures to Size Generalization in Graph Neural Networks
Gilad Yehudai, Ethan Fetaya, Eli Meirom +2
Graph neural networks (GNNs) can process graphs of different sizes, but their ability to generalize across sizes, specifically from small to large graphs, is still not well underst…
Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks
Eli A. Meirom, Haggai Maron, Shie Mannor +1
We consider the problem of controlling a partially-observed dynamic process on a graph by a limited number of interventions. This problem naturally arises in contexts such as sched…
Secondary Vertex Finding in Jets with Neural Networks
Jonathan Shlomi, Sanmay Ganguly, Eilam Gross +5
Jet classification is an important ingredient in measurements and searches for new physics at particle coliders, and secondary vertex reconstruction is a key intermediate step in b…
Auxiliary Learning by Implicit Differentiation
Aviv Navon, Idan Achituve, Haggai Maron +2
Training neural networks with auxiliary tasks is a common practice for improving the performance on a main task of interest. Two main challenges arise in this multi-task learning s…
Self-Supervised Learning for Domain Adaptation on Point-Clouds
Idan Achituve, Haggai Maron, Gal Chechik
Self-supervised learning (SSL) is a technique for learning useful representations from unlabeled data. It has been applied effectively to domain adaptation (DA) on images and video…
Learning Algebraic Multigrid Using Graph Neural Networks
Ilay Luz, Meirav Galun, Haggai Maron +2
Efficient numerical solvers for sparse linear systems are crucial in science and engineering. One of the fastest methods for solving large-scale sparse linear systems is algebraic…
Set2Graph: Learning Graphs From Sets
Hadar Serviansky, Nimrod Segol, Jonathan Shlomi +4
Many problems in machine learning can be cast as learning functions from sets to graphs, or more generally to hypergraphs; in short, Set2Graph functions. Examples include clusterin…
On Learning Sets of Symmetric Elements
Haggai Maron, Or Litany, Gal Chechik +1
Learning from unordered sets is a fundamental learning setup, recently attracting increasing attention. Research in this area has focused on the case where elements of the set are…