SPECT image reconstruction using total variation. - Eldad Haber (Image Processing, 1998)

Fast Finite Volume Simulation of 3D Electromagnetic Problems with Highly Discontinuous Coefficients. - Eldad Haber; Uri M. Ascher (SIAM J. Sci. Comput., 2001)

Computational Methods for Large Distributed Parameter Estimation Problems in 3D. - Uri M. Ascher; Eldad Haber (HPSC, 2003)

Volume Preserving Image Registration. - Eldad Haber; Jan Modersitzki (MICCAI (1), 2004)

Beyond Mutual Information: A Simple and Robust Alternative. - Eldad Haber; Jan Modersitzki (Bildverarbeitung für die Medizin, 2005)

A Scale Space Method for Volume Preserving Image Registration. - Eldad Haber; Jan Modersitzki (Scale-Space, 2005)

Grid-based Image Registration. - William Gropp; Eldad Haber; Stefan Heldmann; David E. Keyes; Neill Miller; Jennifer M. Schopf; Tianzhi Yang (Grid-Based Problem Solving Environments, 2006)

Intensity Gradient Based Registration and Fusion of Multi-modal Images. - Eldad Haber; Jan Modersitzki (MICCAI (2), 2006)

On Effective Methods for Implicit Piecewise Smooth Surface Recovery. - Uri M. Ascher; Eldad Haber; Hui Huang 004 (SIAM J. Sci. Comput., 2006)

A Multilevel Method for Image Registration. - Eldad Haber; Jan Modersitzki (SIAM J. Sci. Comput., 2006)

Image Registration with Guaranteed Displacement Regularity. - Eldad Haber; Jan Modersitzki (Int. J. Comput. Vis., 2007)

An octree multigrid method for quasi-static Maxwell's equations with highly discontinuous coefficients. - Eldad Haber; Stefan Heldmann (J. Comput. Phys., 2007)

An Octree Method for Parametric Image Registration. - Eldad Haber; Stefan Heldmann; Jan Modersitzki (SIAM J. Sci. Comput., 2007)

Iterative Reconstruction of SPECT Images Using Adaptive Multi-level Refinement. - Hanno Schumacher; Stefan Heldmann; Eldad Haber; Bernd Fischer 001 (Bildverarbeitung für die Medizin, 2008)

Adaptive Mesh Refinement for Nonparametric Image Registration. - Eldad Haber; Stefan Heldmann; Jan Modersitzki (SIAM J. Sci. Comput., 2008)

A Scale-Space Approach to Landmark Constrained Image Registration. - Eldad Haber; Stefan Heldmann; Jan Modersitzki (SSVM, 2009)

3D nonrigid registration via optimal mass transport on the GPU. - Tauseef ur Rehman; Eldad Haber; Gallagher Pryor; John Melonakos; Allen R. Tannenbaum (Medical Image Anal., 2009)

Multilevel Algorithms for Large-Scale Interior Point Methods. - Michele Benzi; Eldad Haber; Lauren Taralli (SIAM J. Sci. Comput., 2009)

Fire and smoke detection in video with optimal mass transport based optical flow and neural networks. - Ivan Kolesov; Peter Karasev; Allen R. Tannenbaum; Eldad Haber (ICIP, 2010)

Numerical optimization for constrained image registration. - Eldad Haber; Raya Horesh; Jan Modersitzki (Numer. Linear Algebra Appl., 2010)

New multigrid smoothers for the Oseen problem. - Steven P. Hamilton; Michele Benzi; Eldad Haber (Numer. Linear Algebra Appl., 2010)

An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. - Eldad Haber; Tauseef ur Rehman; Allen R. Tannenbaum (SIAM J. Sci. Comput., 2010)

A preconditioning technique for a class of PDE-constrained optimization problems. - Michele Benzi; Eldad Haber; Lauren Taralli (Adv. Comput. Math., 2011)

A fast method for the solution of the Helmholtz equation. - Eldad Haber; Scott P. MacLachlan (J. Comput. Phys., 2011)

A Second Order Discretization of Maxwell's Equations in the Quasi-Static Regime on OcTree Grids. - Lior Horesh; Eldad Haber (SIAM J. Sci. Comput., 2011)

Numerical methods for A-optimal designs with a sparsity constraint for ill-posed inverse problems. - Eldad Haber; Zhuojun Magnant; Christian Lucero; Luis Tenorio (Comput. Optim. Appl., 2012)

Experimental Design for Biological Systems. - Matthias Chung; Eldad Haber (SIAM J. Control. Optim., 2012)

An Effective Method for Parameter Estimation with PDE Constraints with Multiple Right-Hand Sides. - Eldad Haber; Matthias Chung; Felix Herrmann (SIAM J. Optim., 2012)

Computational Methods in Geophysical Electromagnetics. - Eldad Haber (SIAM, 2014)

Simultaneous Source for non-uniform data variance and missing data. - Eldad Haber; Matthias Chung (CoRR, 2014)

Geophysical Imaging of Fluid Flow in Porous Media. - Jennifer Fohring; Eldad Haber; Lars Ruthotto (SIAM J. Sci. Comput., 2014)

Data mining for real mining: A robust algorithm for prospectivity mapping with uncertainties. - Justin Granek; Eldad Haber (SDM, 2015)

Building an iterative heuristic solver for a quantum annealer. - Gili Rosenberg; Mohammad Vazifeh; Brad Woods; Eldad Haber (Comput. Optim. Appl., 2016)

An oversampling technique for the multiscale finite volume method to simulate electromagnetic responses in the frequency domain. - Luz Angelica Caudillo-Mata; Eldad Haber; Christoph Schwarzbach (CoRR, 2016)

A fast marching algorithm for the factored eikonal equation. - Eran Treister; Eldad Haber (J. Comput. Phys., 2016)

Adaptive A-Optimal Experimental Design for Linear Dynamical Systems. - Jennifer Fohring; Eldad Haber (SIAM/ASA J. Uncertain. Quantification, 2016)

Stable Architectures for Deep Neural Networks. - Eldad Haber; Lars Ruthotto (CoRR, 2017)

Learning across scales - A multiscale method for Convolution Neural Networks. - Eldad Haber; Lars Ruthotto; Elliot Holtham (CoRR, 2017)

A framework for the upscaling of the electrical conductivity in the quasi-static Maxwell's equations. - Luz Angelica Caudillo-Mata; Eldad Haber; Lindsey J. Heagy; Christoph Schwarzbach (J. Comput. Appl. Math., 2017)

jInv-a Flexible Julia Package for PDE Parameter Estimation. - Lars Ruthotto; Eran Treister; Eldad Haber (SIAM J. Sci. Comput., 2017)

Full Waveform Inversion Guided by Travel Time Tomography. - Eran Treister; Eldad Haber (SIAM J. Sci. Comput., 2017)

Reversible Architectures for Arbitrarily Deep Residual Neural Networks. - Bo Chang 002; Lili Meng; Eldad Haber; Lars Ruthotto; David Begert; Elliot Holtham (AAAI, 2018)

Learning Across Scales - Multiscale Methods for Convolution Neural Networks. - Eldad Haber; Lars Ruthotto; Elliot Holtham; Seong-Hwan Jun (AAAI, 2018)

Multi-level Residual Networks from Dynamical Systems View. - Bo Chang 002; Lili Meng; Eldad Haber; Frederick Tung; David Begert (ICLR (Poster), 2018)

GlymphVIS: Visualizing Glymphatic Transport Pathways Using Regularized Optimal Transport. - Rena Elkin; Saad Nadeem; Eldad Haber; Klara Steklova; Hedok Lee; Helene Benveniste; Allen R. Tannenbaum (MICCAI (1), 2018)

Low-Cost Parameterizations of Deep Convolution Neural Networks. - Eran Treister; Lars Ruthotto; Michal Sharoni; Sapir Zafrani; Eldad Haber (CoRR, 2018)

Never look back - A modified EnKF method and its application to the training of neural networks without back propagation. - Eldad Haber; Felix Lucka; Lars Ruthotto (CoRR, 2018)

Multi-resolution neural networks for tracking seismic horizons from few training images. - Bas Peters; Justin Granek; Eldad Haber (CoRR, 2018)

Efficient 3D inversions using the Richards equation. - Rowan Cockett; Lindsey J. Heagy; Eldad Haber (Comput. Geosci., 2018)

An Efficient Algorithm for Matrix-Valued and Vector-Valued Optimal Mass Transport. - Yongxin Chen; Eldad Haber; Kaoru Yamamoto; Tryphon T. Georgiou; Allen R. Tannenbaum (J. Sci. Comput., 2018)

AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks. - Bo Chang 002; Minmin Chen; Eldad Haber; Ed H. Chi (ICLR (Poster), 2019)

IMEXnet A Forward Stable Deep Neural Network. - Eldad Haber; Keegan Lensink; Eran Treister; Lars Ruthotto (ICML, 2019)

3D Joint hydrogeophysical inversion using similarity measures. - Klara Steklova; Eldad Haber (Appl. Math. Comput., 2019)

Automatic classification of geologic units in seismic images using partially interpreted examples. - Bas Peters; Justin Granek; Eldad Haber (CoRR, 2019)

Large-Scale Classification using Multinomial Regression and ADMM. - Samy Wu Fung; Sanna Tyrväinen; Lars Ruthotto; Eldad Haber (CoRR, 2019)

Neural-networks for geophysicists and their application to seismic data interpretation. - Bas Peters; Eldad Haber; Justin Granek (CoRR, 2019)

LeanResNet: A Low-cost yet Effective Convolutional Residual Networks. - Jonathan Ephrath; Lars Ruthotto; Eldad Haber; Eran Treister (CoRR, 2019)

Fully Hyperbolic Convolutional Neural Networks. - Keegan Lensink; Eldad Haber; Bas Peters (CoRR, 2019)

How To Catch A Lion In The Desert - On The Solution Of The Coverage Directed Generation (CDG) Problem. - Raviv Gal; Eldad Haber; Brian Irwin; Bilal Saleh; Avi Ziv (CoRR, 2019)

Fluid Flow Mass Transport for Generative Networks. - Jingrong Lin; Keegan Lensink; Eldad Haber (CoRR, 2019)

Symmetric block-low-rank layers for fully reversible multilevel neural networks. - Bas Peters; Eldad Haber; Keegan Lensink (CoRR, 2019)

A multigrid solver to the Helmholtz equation with a point source based on travel time and amplitude. - Eran Treister; Eldad Haber (Numer. Linear Algebra Appl., 2019)

Using DNNs and Smart Sampling for Coverage Closure Acceleration. - Raviv Gal; Eldad Haber; Avi Ziv (MLCAD, 2020)

Fully reversible neural networks for large-scale surface and sub-surface characterization via remote sensing. - Bas Peters; Eldad Haber; Keegan Lensink (CoRR, 2020)

Segmentation of Pulmonary Opacification in Chest CT Scans of COVID-19 Patients. - Keegan Lensink; Issam H. Laradji; Marco Law; Paolo Emilio Barbano; Savvas Nicolaou; William Parker; Eldad Haber (CoRR, 2020)

Special Issue on the Mathematical Foundations of Deep Learning in Imaging Science. - Joan Bruna; Eldad Haber; Gitta Kutyniok; Thomas Pock; René Vidal (J. Math. Imaging Vis., 2020)

Deep Neural Networks Motivated by Partial Differential Equations. - Lars Ruthotto; Eldad Haber (J. Math. Imaging Vis., 2020)

LeanConvNets: Low-Cost Yet Effective Convolutional Neural Networks. - Jonathan Ephrath; Moshe Eliasof; Lars Ruthotto; Eldad Haber; Eran Treister (IEEE J. Sel. Top. Signal Process., 2020)

Automatic Scalable System for the Coverage-Directed Generation (CDG) Problem. - Raviv Gal; Eldad Haber; Wesam Ibraheem; Brian Irwin; Ziv Nevo; Avi Ziv (DATE, 2021)

Using Deep Neural Networks And Derivative Free Optimization To Accelerate Coverage Closure. - Raviv Gal; Eldad Haber; Brian Irwin; Marwa Mouallem; Bilal Saleh; Avi Ziv (MLCAD, 2021)

PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations. - Moshe Eliasof; Eldad Haber; Eran Treister (NeurIPS, 2021)

An Introduction to Deep Generative Modeling. - Lars Ruthotto; Eldad Haber (CoRR, 2021)

Deep Neural Network Accelerated Implicit Filtering. - Brian Irwin; Eldad Haber; Raviv Gal; Avi Ziv (CoRR, 2021)

A-Optimal Active Learning. - Tue Boesen; Eldad Haber (CoRR, 2021)

Data-driven semi-supervised clustering for oil prediction. - Tue Boesen; Eldad Haber; G. Michael Hoversten (Comput. Geosci., 2021)

pathGCN: Learning General Graph Spatial Operators from Paths. - Moshe Eliasof; Eldad Haber; Eran Treister (ICML, 2022)

Mimetic Neural Networks: A Unified Framework for Protein Design and Folding. - Moshe Eliasof; Tue Boesen; Eldad Haber; Chen Keasar; Eran Treister (Frontiers Bioinform., 2022)

Neural Network Accelerated Implicit Filtering: Integrating Neural Network Surrogates With Provably Convergent Derivative Free Optimization Methods. - Brian Irwin; Eldad Haber; Raviv Gal; Avi Ziv (ICML, 2023)

Semi-Automated Segmentation of Geoscientific Data Using Superpixels. - Conrad P. Koziol; Eldad Haber (CoRR, 2023)

DRIP: Deep Regularizers for Inverse Problems. - Moshe Eliasof; Eldad Haber; Eran Treister (CoRR, 2023)

ADR-GNN: Advection-Diffusion-Reaction Graph Neural Networks. - Moshe Eliasof; Eldad Haber; Eran Treister (CoRR, 2023)

Secant penalized BFGS: a noise robust quasi-Newton method via penalizing the secant condition. - Brian Irwin; Eldad Haber (Comput. Optim. Appl., 2023)

Estimating a Potential Without the Agony of the Partition Function. - Eldad Haber; Moshe Eliasof; Luis Tenorio (SIAM J. Math. Data Sci., 2023)

Feature Transportation Improves Graph Neural Networks. - Moshe Eliasof; Eldad Haber; Eran Treister (AAAI, 2024)

On The Temporal Domain of Differential Equation Inspired Graph Neural Networks. - Moshe Eliasof; Eldad Haber; Eran Treister; Carola-Bibiane Schönlieb (AISTATS, 2024)

Beyond Conventional Parametric Modeling: Data-Driven Framework for Estimation and Prediction of Time Activity Curves in Dynamic PET Imaging. - Niloufar Zakariaei; Arman Rahmim; Eldad Haber (CMMCA@MICCAI, 2024)

Every Node Counts: Improving the Training of Graph Neural Networks on Node Classification. - Moshe Eliasof; Eldad Haber; Eran Treister (ECAI, 2024)

Advection Augmented Convolutional Neural Networks. - Niloufar Zakariaei; Siddharth Rout; Eldad Haber; Moshe Eliasof (NeurIPS, 2024)

An Over Complete Deep Learning Method for Inverse Problems. - Moshe Eliasof; Eldad Haber; Eran Treister (CoRR, 2024)

Graph Neural Networks for Binary Programming. - Moshe Eliasof; Eldad Haber (CoRR, 2024)

Paired Autoencoders for Inverse Problems. - Matthias Chung; Emma Hart; Julianne Chung; Bas Peters; Eldad Haber (CoRR, 2024)

Deep Optimal Experimental Design for Parameter Estimation Problems. - Md Shahriar Rahim Siddiqui; Arman Rahmim; Eldad Haber (CoRR, 2024)

Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data. - Bas Peters; Eldad Haber; Keegan Lensink (CoRR, 2024)

DAWN-SI: Data-Aware and Noise-Informed Stochastic Interpolation for Solving Inverse Problems. - Shadab Ahamed; Eldad Haber (CoRR, 2024)

Graph Neural Reaction Diffusion Models. - Moshe Eliasof; Eldad Haber; Eran Treister (SIAM J. Sci. Comput., 2024)

Learning Regularization for Graph Inverse Problems. - Moshe Eliasof; Md Shahriar Rahim Siddiqui; Carola-Bibiane Schönlieb; Eldad Haber (AAAI, 2025)

Inversion of Magnetic Data Using Learned Dictionaries and Scale Space. - Shadab Ahamed; Simon Ghyselincks; Pablo Chang Huang Arias; Julian Kloiber; Yasin Ranjbar; Jingrong Tang; Niloufar Zakariaei; Eldad Haber (SSVM (2), 2025)

Neural DAEs: Constrained Neural Networks. - Tue Boesen; Eldad Haber; Uri M. Ascher (SIAM J. Sci. Comput., 2025)

Multiscale Training of Convolutional Neural Networks. - Niloufar Zakariaei; Shadab Ahamed; Eldad Haber; Moshe Eliasof (CoRR, 2025)

Iterative Flow Matching - Path Correction and Gradual Refinement for Enhanced Generative Modeling. - Eldad Haber; Shadab Ahamed; Md Shahriar Rahim Siddiqui; Niloufar Zakariaei; Moshe Eliasof (CoRR, 2025)

Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data. - Siddharth Rout; Eldad Haber; Stéphane Gaudreault (CoRR, 2025)

Towards Efficient Training of Graph Neural Networks: A Multiscale Approach. - Eshed Gal; Moshe Eliasof; Carola-Bibiane Schönlieb; Eldad Haber; Eran Treister (CoRR, 2025)

Graph Flow Matching: Enhancing Image Generation with Neighbor-Aware Flow Fields. - Md Shahriar Rahim Siddiqui; Moshe Eliasof; Eldad Haber (CoRR, 2025)

Synthetic Geology - Structural Geology Meets Deep Learning. - Simon Ghyselincks; Valeriia Okhmak; Stefano Zampini; George Turkiyyah; David E. Keyes; Eldad Haber (CoRR, 2025)

Flow Matching for Probabilistic Learning of Dynamical Systems from Missing or Noisy Data. - Siddharth Rout; Eldad Haber; Stéphane Gaudreault (CoRR, 2025)

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows. - Moshe Eliasof; Eldad Haber; Carola-Bibiane Schönlieb (CoRR, 2025)