image 1

Haggai Maron

I am a Research Scientist at NVIDIA Research. My main fields of interest are machine learning, optimization and shape analysis. More specifically, I am working on applying deep learning to irregular domains (e.g., graphs, point clouds, and surfaces) and graph/shape matching problems. I completed my Ph.D. in 2019 at the Department of Computer Science and Applied Mathematics at the Weizmann Institute of Science under the supervision of Prof. Yaron Lipman.
My CV can be found here.

Email: hmaron (at) nvidia.com, Google scholar page, GitHub page

News

Teaching

  • 2019/spring (WIS): Geometric and Algebraic Methods in Deep Learning
  • 2018/winter (WIS): Geometry and Deep Learning


Publications

Position-Agnostic Multi-Microphone Speech Dereverberation

Yochai Yemini, Ethan Fetaya, Haggai Maron, Sharon Gannot
Preprint, 2020

Abstract Paper

On Size Generalization in Graph Neural Networks

Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, Haggai Maron
Preprint, 2020

Abstract Paper

On the Universality of Rotation Equivariant Point Cloud Networks

Nadav Dym, Haggai Maron
Preprint, 2020

Abstract Paper

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

Eli A Meirom, Haggai Maron, Shie Mannor, Gal Chechik
Preprint, 2020

Abstract Paper

Auxiliary Learning by Implicit Differentiation

Aviv Navon*, Idan Achituve*, Haggai Maron, Gal Chechik**, Ethan Fetaya** (*/** equal contribution)
Preprint, 2020

Abstract Paper GitHub

Self-Supervised Learning for Domain Adaptation on Point-Clouds

Idan Achituve, Haggai Maron, Gal Chechik
Winter Conference on Applications of Computer Vision (WACV), 2021

Abstract Paper GitHub

Set2Graph: Learning Graphs From Sets

Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman
34th Annual Conference on Neural Information Processing Systems (NeurIPS 2020)

Abstract Paper Code

Secondary Vertex Finding in Jets with Neural Networks

Jonathan Shlomi, Sanmay Ganguly, Eilam Gross, Kyle Cranmer, Yaron Lipman, Hadar Serviansky, Haggai Maron, Nimrod Segol
Technical report, 2020

Abstract Paper

On Learning Sets of Symmetric Elements

Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya
International Conference on Machine Learning (ICML) 2020

ICML 2020 outstanding paper award

Abstract Paper Preliminary code Video Slides Interview

Learning Algebraic Multigrid Using Graph Neural Networks

Ilay Luz, Meirav Galun, Haggai Maron, Ronen Basri, Irad Yavneh
International Conference on Machine Learning (ICML) 2020

Abstract Paper Video+Slides

Open Problems: Approximation Power of Invariant Graph Networks

Haggai Maron, Heli Ben-Hamu, Yaron Lipman
NeurIPS 2019 Graph Representation Learning Workshop

Abstract Paper Slides Talk (go to 1:15:00)

Ph.D. Thesis

Haggai Maron
Weizmann Institute of Science, 2019

Abstract Paper

Deep and Convex Shape Analysis

Haggai Maron
SIGGRAPH 2019 Doctoral Consortium

Abstract Paper Poster

Provably Powerful Graph Networks

Haggai Maron*, Heli Ben-Hamu*, Hadar Serviansky*, Yaron Lipman (*equal contribution)
33rd Annual Conference on Neural Information Processing Systems (NeurIPS 2019)

Abstract Arxiv GitHub (TensorFlow) GitHub (PyTorch) Blog post Poster

Controlling Neural Level Sets

Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, Yaron Lipman
33rd Annual Conference on Neural Information Processing Systems (NeurIPS 2019)

Abstract Arxiv Code Poster

Surface Networks via General Covers

Niv Haim*, Nimrod Segol*, Heli Ben-Hamu, Haggai Maron, Yaron Lipman (*equal contribution)
International Conference on Computer Vision (ICCV) 2019

Abstract Arxiv

On the Universality of Invariant Networks

Haggai Maron, Ethan Fetaya, Nimrod Segol, Yaron Lipman
International Conference on Machine Learning (ICML) 2019

Abstract Arxiv Poster Video Blog post
image 1

Invariant and Equivariant Graph Networks

Haggai Maron, Heli Ben-Hamu, Nadav Shamir and Yaron Lipman
International Conference on Learning Representations (ICLR) 2019

Abstract Arxiv GitHub Poster Blog post
image 1

Sinkhorn Algorithm for Lifted Assignment Problems

Yam Kushinsky, Haggai Maron, Nadav Dym and Yaron Lipman
SIAM Journal on Imaging Sciences, 2019

Abstract Arxiv
image 1

(Probably) Concave Graph Matching

Haggai Maron and Yaron Lipman
32nd Annual Conference on Neural Information Processing Systems (NeurIPS 2018)

spotlight presentation (3.5% acceptance rate)

Abstract Arxiv Poster Short Video GitHub
image 1

Multi-chart Generative Surface Modeling

Heli Ben-Hamu, Haggai Maron, Itay Kezurer, Gal Avineri and Yaron Lipman
ACM SIGGRAPH Asia 2018

Abstract Arxiv GitHub
image 1

Point Convolutional Neural Networks by Extension Operators

Matan Atzmon*, Haggai Maron* and Yaron Lipman (*equal contribution)
ACM SIGGRAPH 2018

Abstract Arxiv GitHub Slides
image 1

DS++: A Flexible, Scalable and Provably Tight Relaxation for Matching Problems

Nadav Dym*, Haggai Maron* and Yaron Lipman (*equal contribution)
ACM SIGGRAPH ASIA 2017

Abstract Arxiv GitHub Slides
image 1

Convolutional Neural Networks on Surfaces via Seamless Toric Covers

Haggai Maron, Meirav Galun, Noam Aigerman, Miri Trope, Nadav Dym, Ersin Yumer, Vladimir G. Kim and Yaron Lipman
ACM SIGGRAPH 2017

Abstract Paper (low res) GitHub Data Slides Video
image 1

Point Registration via Efficient Convex Relaxation

Haggai Maron, Nadav Dym, Itay Kezurer, Shahar Kovalsky and Yaron Lipman
ACM SIGGRAPH 2016

Abstract Paper (low res) GitHub Slides Video
image 1

Passive Light and Viewpoint Sensitive Display of 3D Content

Anat Levin, Haggai Maron and Michal Yarom
International Conference on Computational Photography (ICCP) 2016

Abstract Paper Paper+Supplementary