b'Yaron Lipman'

research

∙ 06/11/2023

On Kinetic Optimal Probability Paths for Generative Models

Recent successful generative models are trained by fitting a neural netw...

0 Neta Shaul, et al. ∙

research

∙ 04/28/2023

Multisample Flow Matching: Straightening Flows with Minibatch Couplings

Simulation-free methods for training continuous-time generative models c...

1 Aram-Alexandre Pooladian, et al. ∙

research

∙ 03/25/2023

VisCo Grids: Surface Reconstruction with Viscosity and Coarea Grids

Surface reconstruction has been seeing a lot of progress lately by utili...

6 Albert Pumarola, 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

∙ 02/16/2023

MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation

Recent advances in text-to-image generation with diffusion models presen...

11 Omer Bar-Tal, et al. ∙

research

∙ 02/07/2023

Riemannian Flow Matching on General Geometries

We propose Riemannian Flow Matching (RFM), a simple yet powerful framewo...

0 Ricky T. Q. Chen, et al. ∙

research

∙ 10/06/2022

Flow Matching for Generative Modeling

We introduce a new paradigm for generative modeling built on Continuous ...

2 Yaron Lipman, et al. ∙

research

∙ 10/04/2022

Neural Conservation Laws: A Divergence-Free Perspective

We investigate the parameterization of deep neural networks that by desi...

0 Jack Richter-Powell, et al. ∙

research

∙ 07/11/2022

Matching Normalizing Flows and Probability Paths on Manifolds

Continuous Normalizing Flows (CNFs) are a class of generative models tha...

12 Heli Ben-Hamu, 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

∙ 08/18/2021

Moser Flow: Divergence-based Generative Modeling on Manifolds

We are interested in learning generative models for complex geometries d...

0 Noam Rozen, et al. ∙

research

∙ 06/22/2021

Volume Rendering of Neural Implicit Surfaces

Neural volume rendering became increasingly popular recently due to its ...

6 Lior Yariv, et al. ∙

research

∙ 06/18/2021

Riemannian Convex Potential Maps

Modeling distributions on Riemannian manifolds is a crucial component in...

6 Samuel Cohen, et al. ∙

research

∙ 06/14/2021

Phase Transitions, Distance Functions, and Implicit Neural Representations

Representing surfaces as zero level sets of neural networks recently eme...

0 Yaron Lipman, et al. ∙

research

∙ 06/14/2020

From Graph Low-Rank Global Attention to 2-FWL Approximation

Graph Neural Networks (GNNs) are known to have an expressive power bound...

0 Omri Puny, 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

∙ 10/06/2019

On Universal Equivariant Set Networks

Using deep neural networks that are either invariant or equivariant to p...

9 Nimrod Segol, 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

∙ 11/06/2017

A Linear Variational Principle for Riemann Mappings and Discrete Conformality

We consider Riemann mappings from bounded Lipschitz domains in the plane...

0 Nadav Dym, et al. ∙

research

∙ 11/06/2017

A Linear Variational Principle for Riemann Mapping and Discrete Conformality

We consider Riemann mappings from bounded Lipschitz domains in the plane...

0 Nadav Dym, et al. ∙

research

∙ 06/25/2017

Photometric Stereo by Hemispherical Metric Embedding

Photometric Stereo methods seek to reconstruct the 3d shape of an object...

0 Ofer Bartal, 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. ∙

research

∙ 06/10/2015

Wide baseline stereo matching with convex bounded-distortion constraints

Finding correspondences in wide baseline setups is a challenging problem...

0 Meirav Galun, et al. ∙

research

∙ 01/27/2013

Approximation of Polyhedral Surface Uniformization

We present a constructive approach for approximating the conformal map (...

0 Yaron Lipman, et al. ∙

Yaron Lipman

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