b'Eldad Haber'

research

∙ 07/29/2023

ADR-GNN: Advection-Diffusion-Reaction Graph Neural Networks

Graph neural networks (GNNs) have shown remarkable success in learning r...

0 Moshe Eliasof, et al. ∙

research

∙ 03/30/2023

DRIP: Deep Regularizers for Inverse Problems

Inverse problems are mathematically ill-posed. Thus, given some (noisy) ...

0 Moshe Eliasof, et al. ∙

research

∙ 03/20/2023

Semi-Automated Segmentation of Geoscientific Data Using Superpixels

Geological processes determine the distribution of resources such as cri...

0 Conrad P. Koziol, et al. ∙

research

∙ 11/29/2022

Every Node Counts: Improving the Training of Graph Neural Networks on Node Classification

Graph Neural Networks (GNNs) are prominent in handling sparse and unstru...

0 Moshe Eliasof, et al. ∙

research

∙ 11/25/2022

Neural DAEs: Constrained neural networks

In this article we investigate the effect of explicitly adding auxiliary...

0 Tue Boesen, et al. ∙

research

∙ 08/19/2022

Estimating a potential without the agony of the partition function

Estimating a Gibbs density function given a sample is an important probl...

0 Eldad Haber, et al. ∙

research

∙ 07/15/2022

pathGCN: Learning General Graph Spatial Operators from Paths

Graph Convolutional Networks (GCNs), similarly to Convolutional Neural N...

0 Moshe Eliasof, et al. ∙

research

∙ 10/18/2021

A-Optimal Active Learning

In this work we discuss the problem of active learning. We present an ap...

0 Tue Boesen, et al. ∙

research

∙ 08/04/2021

PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations

Graph neural networks are increasingly becoming the go-to approach in va...

0 Moshe Eliasof, et al. ∙

research

∙ 03/09/2021

An Introduction to Deep Generative Modeling

Deep generative models (DGM) are neural networks with many hidden layers...

100 Lars Ruthotto, et al. ∙

research

∙ 02/07/2021

Mimetic Neural Networks: A unified framework for Protein Design and Folding

Recent advancements in machine learning techniques for protein folding m...

0 Moshe Eliasof, et al. ∙

research

∙ 10/03/2020

Secant Penalized BFGS: A Noise Robust Quasi-Newton Method Via Penalizing The Secant Condition

In this paper, we introduce a new variant of the BFGS method designed to...

0 Brian Irwin, et al. ∙

research

∙ 07/07/2020

Segmentation of Pulmonary Opacification in Chest CT Scans of COVID-19 Patients

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has rap...

18 Keegan Lensink, et al. ∙

research

∙ 03/16/2020

Fully reversible neural networks for large-scale surface and sub-surface characterization via remote sensing

The large spatial/frequency scale of hyperspectral and airborne magnetic...

0 Bas Peters, et al. ∙

research

∙ 10/29/2019

LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have become indispensable for solvi...

29 Jonathan Ephrath, et al. ∙

research

∙ 10/03/2019

Fluid Flow Mass Transport for Generative Networks

Generative Adversarial Networks have been shown to be powerful in genera...

0 Jingrong Lin, et al. ∙

research

∙ 05/24/2019

Fully Hyperbolic Convolutional Neural Networks

Convolutional Neural Networks (CNN) have recently seen tremendous succes...

0 Keegan Lensink, et al. ∙

research

∙ 04/15/2019

LeanResNet: A Low-cost yet Effective Convolutional Residual Networks

Convolutional Neural Networks (CNNs) filter the input data using a serie...

0 Jonathan Ephrath, et al. ∙

research

∙ 03/27/2019

Neural-networks for geophysicists and their application to seismic data interpretation

Neural-networks have seen a surge of interest for the interpretation of ...

0 Bas Peters, et al. ∙

research

∙ 03/06/2019

IMEXnet: A Forward Stable Deep Neural Network

Deep convolutional neural networks have revolutionized many machine lear...

0 Eldad Haber, et al. ∙

research

∙ 02/26/2019

AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks

Recurrent neural networks have gained widespread use in modeling sequent...

24 Bo Chang, et al. ∙

research

∙ 01/27/2019

Large-Scale Classification using Multinomial Regression and ADMM

We present a novel method for learning the weights in multinomial logist...

0 Samy Wu Fung, et al. ∙

research

∙ 01/12/2019

Automatic classification of geologic units in seismic images using partially interpreted examples

Geologic interpretation of large seismic stacked or migrated seismic ima...

0 Bas Peters, et al. ∙

research

∙ 12/26/2018

Multi-resolution neural networks for tracking seismic horizons from few training images

Detecting a specific horizon in seismic images is a valuable tool for ge...

0 Bas Peters, et al. ∙

research

∙ 08/24/2018

GlymphVIS: Visualizing Glymphatic Transport Pathways Using Regularized Optimal Transport

The glymphatic system (GS) is a transit passage that facilitates brain m...

0 Rena Elkin, et al. ∙

research

∙ 05/21/2018

Never look back - A modified EnKF method and its application to the training of neural networks without back propagation

In this work, we present a new derivative-free optimization method and i...

0 Eldad Haber, et al. ∙

research

∙ 05/21/2018

Never look back - The EnKF method and its application to the training of neural networks without back propagation

In this work, we present a new derivative-free optimization method and i...

0 Eldad Haber, et al. ∙

research

∙ 05/20/2018

Low-Cost Parameterizations of Deep Convolutional Neural Networks

Convolutional Neural Networks (CNNs) filter the input data using a serie...

0 Eran Treister, et al. ∙

research

∙ 05/20/2018

Low-Cost Parameterizations of Deep Convolution Neural Networks

The main computational cost in the training of and prediction with Convo...

0 Eran Treister, et al. ∙

research

∙ 04/23/2018

Simultaneous shot inversion for nonuniform geometries using fast data interpolation

Stochastic optimization is key to efficient inversion in PDE-constrained...

0 Michelle Liu, et al. ∙

research

∙ 04/12/2018

Deep Neural Networks motivated by Partial Differential Equations

Partial differential equations (PDEs) are indispensable for modeling man...

0 Lars Ruthotto, et al. ∙

research

∙ 12/17/2017

A multigrid solver to the Helmholtz equation with a point source based on travel time and amplitude

The Helmholtz equation arises when modeling wave propagation in the freq...

0 Eran Treister, et al. ∙

research

∙ 10/27/2017

Multi-level Residual Networks from Dynamical Systems View

Deep residual networks (ResNets) and their variants are widely used in m...

0 Bo Chang, et al. ∙

research

∙ 09/12/2017

Reversible Architectures for Arbitrarily Deep Residual Neural Networks

Recently, deep residual networks have been successfully applied in many ...

0 Bo Chang, et al. ∙

research

∙ 06/26/2017

An Efficient Algorithm for Matrix-Valued and Vector-Valued Optimal Mass Transport

We present an efficient algorithm for recent generalizations of optimal ...

0 Yongxin Chen, et al. ∙

research

∙ 05/09/2017

Stable Architectures for Deep Neural Networks

Deep neural networks have become invaluable tools for supervised machine...

0 Eldad Haber, et al. ∙

research

∙ 03/06/2017

Learning across scales - A multiscale method for Convolution Neural Networks

In this work we establish the relation between optimal control and train...

0 Eldad Haber, et al. ∙

research

∙ 06/23/2016

jInv -- a flexible Julia package for PDE parameter estimation

Estimating parameters of Partial Differential Equations (PDEs) from nois...

0 Lars Ruthotto, et al. ∙

Eldad Haber

Featured Co-authors