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2020 – today
- 2024
[j37]Moshe Eliasof
, Eldad Haber, Eran Treister:
Graph Neural Reaction Diffusion Models. SIAM J. Sci. Comput. 46(4): 399- (2024)- [c27]Moshe Eliasof, Eldad Haber, Eran Treister:
Feature Transportation Improves Graph Neural Networks. AAAI 2024: 11874-11882 - [c26]Moshe Eliasof, Eldad Haber, Eran Treister, Carola-Bibiane Schönlieb:
On The Temporal Domain of Differential Equation Inspired Graph Neural Networks. AISTATS 2024: 1792-1800 - [c25]Niloufar Zakariaei, Arman Rahmim, Eldad Haber:
Beyond Conventional Parametric Modeling: Data-Driven Framework for Estimation and Prediction of Time Activity Curves in Dynamic PET Imaging. CMMCA@MICCAI 2024: 99-109 - [c24]Moshe Eliasof, Eldad Haber, Eran Treister:
Every Node Counts: Improving the Training of Graph Neural Networks on Node Classification. ECAI 2024: 2508-2515 - [i54]Moshe Eliasof, Eldad Haber, Eran Treister, Carola-Bibiane Schönlieb:
On The Temporal Domain of Differential Equation Inspired Graph Neural Networks. CoRR abs/2401.11074 (2024) - [i53]Moshe Eliasof, Eldad Haber, Eran Treister:
An Over Complete Deep Learning Method for Inverse Problems. CoRR abs/2402.04653 (2024) - [i52]Moshe Eliasof, Eldad Haber:
Graph Neural Networks for Binary Programming. CoRR abs/2404.04874 (2024) - [i51]Matthias Chung, Emma Hart, Julianne Chung, Bas Peters, Eldad Haber:
Paired Autoencoders for Inverse Problems. CoRR abs/2405.13220 (2024) - [i50]Niloufar Zakariaei, Arman Rahmim, Eldad Haber:
Beyond Conventional Parametric Modeling: Data-Driven Framework for Estimation and Prediction of Time Activity Curves in Dynamic PET Imaging. CoRR abs/2405.21021 (2024) - [i49]Moshe Eliasof, Eldad Haber, Eran Treister:
Graph Neural Reaction Diffusion Models. CoRR abs/2406.10871 (2024) - [i48]Md Shahriar Rahim Siddiqui, Arman Rahmim, Eldad Haber:
Deep Optimal Experimental Design for Parameter Estimation Problems. CoRR abs/2406.14003 (2024) - [i47]Niloufar Zakariaei, Siddharth Rout, Eldad Haber, Moshe Eliasof:
Advection Augmented Convolutional Neural Networks. CoRR abs/2406.19253 (2024) - [i46]Bas Peters, Eldad Haber, Keegan Lensink:
Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data. CoRR abs/2407.00595 (2024) - [i45]Moshe Eliasof, Md Shahriar Rahim Siddiqui, Carola-Bibiane Schönlieb, Eldad Haber:
Learning Regularization for Graph Inverse Problems. CoRR abs/2408.10436 (2024) - 2023
- [j36]Brian Irwin, Eldad Haber:
Secant penalized BFGS: a noise robust quasi-Newton method via penalizing the secant condition. Comput. Optim. Appl. 84(3): 651-702 (2023) - [j35]Eldad Haber, Moshe Eliasof, Luis Tenorio:
Estimating a Potential Without the Agony of the Partition Function. SIAM J. Math. Data Sci. 5(4): 1005-1027 (2023) - [c23]Brian Irwin, Eldad Haber, Raviv Gal, Avi Ziv:
Neural Network Accelerated Implicit Filtering: Integrating Neural Network Surrogates With Provably Convergent Derivative Free Optimization Methods. ICML 2023: 14376-14389 - [i44]Conrad P. Koziol, Eldad Haber:
Semi-Automated Segmentation of Geoscientific Data Using Superpixels. CoRR abs/2303.11404 (2023) - [i43]Moshe Eliasof, Eldad Haber, Eran Treister:
DRIP: Deep Regularizers for Inverse Problems. CoRR abs/2304.00015 (2023) - [i42]Moshe Eliasof, Eldad Haber, Eran Treister:
ADR-GNN: Advection-Diffusion-Reaction Graph Neural Networks. CoRR abs/2307.16092 (2023) - 2022
- [j34]Moshe Eliasof, Tue Boesen, Eldad Haber, Chen Keasar, Eran Treister:
Mimetic Neural Networks: A Unified Framework for Protein Design and Folding. Frontiers Bioinform. 2 (2022) - [c22]Moshe Eliasof, Eldad Haber, Eran Treister:
pathGCN: Learning General Graph Spatial Operators from Paths. ICML 2022: 5878-5891 - [i41]Moshe Eliasof, Eldad Haber, Eran Treister:
pathGCN: Learning General Graph Spatial Operators from Paths. CoRR abs/2207.07408 (2022) - [i40]Eldad Haber, Moshe Eliasof, Luis Tenorio:
Estimating a potential without the agony of the partition function. CoRR abs/2208.09433 (2022) - [i39]Tue Boesen, Eldad Haber, Uri M. Ascher:
Neural DAEs: Constrained neural networks. CoRR abs/2211.14302 (2022) - [i38]Moshe Eliasof, Eldad Haber, Eran Treister:
Every Node Counts: Improving the Training of Graph Neural Networks on Node Classification. CoRR abs/2211.16631 (2022) - 2021
- [j33]Tue Boesen, Eldad Haber, G. Michael Hoversten:
Data-driven semi-supervised clustering for oil prediction. Comput. Geosci. 148: 104684 (2021) - [c21]Raviv Gal, Eldad Haber, Wesam Ibraheem, Brian Irwin, Ziv Nevo, Avi Ziv:
Automatic Scalable System for the Coverage-Directed Generation (CDG) Problem. DATE 2021: 206-211 - [c20]Raviv Gal, Eldad Haber, Brian Irwin, Marwa Mouallem, Bilal Saleh, Avi Ziv:
Using Deep Neural Networks And Derivative Free Optimization To Accelerate Coverage Closure. MLCAD 2021: 1-6 - [c19]Moshe Eliasof, Eldad Haber, Eran Treister:
PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations. NeurIPS 2021: 3836-3849 - [i37]Moshe Eliasof, Tue Boesen, Eldad Haber, Chen Keasar, Eran Treister:
Mimetic Neural Networks: A unified framework for Protein Design and Folding. CoRR abs/2102.03881 (2021) - [i36]Lars Ruthotto, Eldad Haber:
An Introduction to Deep Generative Modeling. CoRR abs/2103.05180 (2021) - [i35]Brian Irwin, Eldad Haber, Raviv Gal, Avi Ziv:
Deep Neural Network Accelerated Implicit Filtering. CoRR abs/2105.08883 (2021) - [i34]Moshe Eliasof, Eldad Haber, Eran Treister:
PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations. CoRR abs/2108.01938 (2021) - [i33]Tue Boesen, Eldad Haber:
A-Optimal Active Learning. CoRR abs/2110.09585 (2021) - 2020
- [j32]Joan Bruna, Eldad Haber, Gitta Kutyniok, Thomas Pock, René Vidal:
Special Issue on the Mathematical Foundations of Deep Learning in Imaging Science. J. Math. Imaging Vis. 62(3): 277-278 (2020) - [j31]Lars Ruthotto, Eldad Haber:
Deep Neural Networks Motivated by Partial Differential Equations. J. Math. Imaging Vis. 62(3): 352-364 (2020) - [j30]Jonathan Ephrath, Moshe Eliasof, Lars Ruthotto, Eldad Haber, Eran Treister:
LeanConvNets: Low-Cost Yet Effective Convolutional Neural Networks. IEEE J. Sel. Top. Signal Process. 14(4): 894-904 (2020) - [c18]Raviv Gal, Eldad Haber, Avi Ziv:
Using DNNs and Smart Sampling for Coverage Closure Acceleration. MLCAD 2020: 15-20 - [i32]Bas Peters, Eldad Haber, Keegan Lensink:
Fully reversible neural networks for large-scale surface and sub-surface characterization via remote sensing. CoRR abs/2003.07474 (2020) - [i31]Keegan Lensink, Issam H. Laradji, Marco Law, Paolo Emilio Barbano, Savvas Nicolaou, William Parker, Eldad Haber:
Segmentation of Pulmonary Opacification in Chest CT Scans of COVID-19 Patients. CoRR abs/2007.03643 (2020) - [i30]Brian Irwin, Eldad Haber:
Secant Penalized BFGS: A Noise Robust Quasi-Newton Method Via Penalizing The Secant Condition. CoRR abs/2010.01275 (2020)
2010 – 2019
- 2019
- [j29]Klara Steklova, Eldad Haber:
3D Joint hydrogeophysical inversion using similarity measures. Appl. Math. Comput. 357: 338-356 (2019) - [j28]Eran Treister, Eldad Haber:
A multigrid solver to the Helmholtz equation with a point source based on travel time and amplitude. Numer. Linear Algebra Appl. 26(1) (2019) - [c17]Bo Chang, Minmin Chen, Eldad Haber, Ed H. Chi:
AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks. ICLR (Poster) 2019 - [c16]Eldad Haber, Keegan Lensink, Eran Treister, Lars Ruthotto:
IMEXnet A Forward Stable Deep Neural Network. ICML 2019: 2525-2534 - [i29]Bas Peters, Justin Granek, Eldad Haber:
Automatic classification of geologic units in seismic images using partially interpreted examples. CoRR abs/1901.03786 (2019) - [i28]Samy Wu Fung, Sanna Tyrväinen, Lars Ruthotto, Eldad Haber:
Large-Scale Classification using Multinomial Regression and ADMM. CoRR abs/1901.09450 (2019) - [i27]Bo Chang, Minmin Chen, Eldad Haber, Ed H. Chi:
AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks. CoRR abs/1902.09689 (2019) - [i26]Eldad Haber, Keegan Lensink, Eran Treister, Lars Ruthotto:
IMEXnet: A Forward Stable Deep Neural Network. CoRR abs/1903.02639 (2019) - [i25]Bas Peters, Eldad Haber, Justin Granek:
Neural-networks for geophysicists and their application to seismic data interpretation. CoRR abs/1903.11215 (2019) - [i24]Jonathan Ephrath, Lars Ruthotto, Eldad Haber, Eran Treister:
LeanResNet: A Low-cost yet Effective Convolutional Residual Networks. CoRR abs/1904.06952 (2019) - [i23]Keegan Lensink, Eldad Haber, Bas Peters:
Fully Hyperbolic Convolutional Neural Networks. CoRR abs/1905.10484 (2019) - [i22]Raviv Gal, Eldad Haber, Brian Irwin, Bilal Saleh, Avi Ziv:
How To Catch A Lion In The Desert - On The Solution Of The Coverage Directed Generation (CDG) Problem. CoRR abs/1910.00170 (2019) - [i21]Jingrong Lin, Keegan Lensink, Eldad Haber:
Fluid Flow Mass Transport for Generative Networks. CoRR abs/1910.01694 (2019) - [i20]Jonathan Ephrath, Moshe Eliasof, Lars Ruthotto, Eldad Haber, Eran Treister:
LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks. CoRR abs/1910.13157 (2019) - [i19]Bas Peters, Eldad Haber, Keegan Lensink:
Symmetric block-low-rank layers for fully reversible multilevel neural networks. CoRR abs/1912.12137 (2019) - 2018
- [j27]Rowan Cockett, Lindsey J. Heagy, Eldad Haber:
Efficient 3D inversions using the Richards equation. Comput. Geosci. 116: 91-102 (2018) - [j26]Yongxin Chen, Eldad Haber, Kaoru Yamamoto, Tryphon T. Georgiou, Allen R. Tannenbaum:
An Efficient Algorithm for Matrix-Valued and Vector-Valued Optimal Mass Transport. J. Sci. Comput. 77(1): 79-100 (2018) - [c15]Bo Chang, Lili Meng, Eldad Haber, Lars Ruthotto, David Begert, Elliot Holtham:
Reversible Architectures for Arbitrarily Deep Residual Neural Networks. AAAI 2018: 2811-2818 - [c14]Eldad Haber, Lars Ruthotto, Elliot Holtham, Seong-Hwan Jun:
Learning Across Scales - Multiscale Methods for Convolution Neural Networks. AAAI 2018: 3142-3148 - [c13]Bo Chang, Lili Meng, Eldad Haber, Frederick Tung, David Begert:
Multi-level Residual Networks from Dynamical Systems View. ICLR (Poster) 2018 - [c12]Rena Elkin, Saad Nadeem, Eldad Haber, Klara Steklova, Hedok Lee, Helene Benveniste, Allen R. Tannenbaum:
GlymphVIS: Visualizing Glymphatic Transport Pathways Using Regularized Optimal Transport. MICCAI (1) 2018: 844-852 - [i18]Lars Ruthotto, Eldad Haber:
Deep Neural Networks motivated by Partial Differential Equations. CoRR abs/1804.04272 (2018) - [i17]Eran Treister, Lars Ruthotto, Michal Sharoni, Sapir Zafrani, Eldad Haber:
Low-Cost Parameterizations of Deep Convolution Neural Networks. CoRR abs/1805.07821 (2018) - [i16]Eldad Haber, Felix Lucka, Lars Ruthotto:
Never look back - A modified EnKF method and its application to the training of neural networks without back propagation. CoRR abs/1805.08034 (2018) - [i15]Rena Elkin, Saad Nadeem, Eldad Haber, Klara Steklova, Hedok Lee, Helene Benveniste, Allen R. Tannenbaum:
GlymphVIS: Visualizing Glymphatic Transport Pathways Using Regularized Optimal Transport. CoRR abs/1808.08304 (2018) - [i14]Bas Peters, Justin Granek, Eldad Haber:
Multi-resolution neural networks for tracking seismic horizons from few training images. CoRR abs/1812.11092 (2018) - 2017
- [j25]Luz Angelica Caudillo-Mata, Eldad Haber, Lindsey J. Heagy, Christoph Schwarzbach:
A framework for the upscaling of the electrical conductivity in the quasi-static Maxwell's equations. J. Comput. Appl. Math. 317: 388-402 (2017) - [j24]Lars Ruthotto, Eran Treister, Eldad Haber:
jInv-a Flexible Julia Package for PDE Parameter Estimation. SIAM J. Sci. Comput. 39(5) (2017) - [j23]Eran Treister, Eldad Haber:
Full Waveform Inversion Guided by Travel Time Tomography. SIAM J. Sci. Comput. 39(5) (2017) - [i13]Eldad Haber, Lars Ruthotto, Elliot Holtham:
Learning across scales - A multiscale method for Convolution Neural Networks. CoRR abs/1703.02009 (2017) - [i12]Eldad Haber, Lars Ruthotto:
Stable Architectures for Deep Neural Networks. CoRR abs/1705.03341 (2017) - [i11]Yongxin Chen, Eldad Haber, Kaoru Yamamoto, Tryphon T. Georgiou, Allen R. Tannenbaum:
An Efficient Algorithm for Matrix-Valued and Vector-Valued Optimal Mass Transport. CoRR abs/1706.08841 (2017) - [i10]Bo Chang, Lili Meng, Eldad Haber, Lars Ruthotto, David Begert, Elliot Holtham:
Reversible Architectures for Arbitrarily Deep Residual Neural Networks. CoRR abs/1709.03698 (2017) - [i9]Bo Chang, Lili Meng, Eldad Haber, Frederick Tung, David Begert:
Multi-level Residual Networks from Dynamical Systems View. CoRR abs/1710.10348 (2017) - [i8]Eran Treister, Eldad Haber:
A multigrid solver to the Helmholtz equation with a point source based on travel time and amplitude. CoRR abs/1712.06091 (2017) - 2016
- [j22]Gili Rosenberg, Mohammad Vazifeh, Brad Woods, Eldad Haber:
Building an iterative heuristic solver for a quantum annealer. Comput. Optim. Appl. 65(3): 845-869 (2016) - [j21]Eran Treister, Eldad Haber:
A fast marching algorithm for the factored eikonal equation. J. Comput. Phys. 324: 210-225 (2016) - [j20]Jennifer Fohring, Eldad Haber:
Adaptive A-Optimal Experimental Design for Linear Dynamical Systems. SIAM/ASA J. Uncertain. Quantification 4(1): 1138-1159 (2016) - [i7]Lars Ruthotto, Eran Treister, Eldad Haber:
jInv - a flexible Julia package for PDE parameter estimation. CoRR abs/1606.07399 (2016) - [i6]Eran Treister, Eldad Haber:
Full waveform inversion guided by travel time tomography. CoRR abs/1607.00968 (2016) - [i5]Eran Treister, Eldad Haber:
A fast marching algorithm for the factored eikonal equation. CoRR abs/1607.00973 (2016) - [i4]Luz Angelica Caudillo-Mata, Eldad Haber, Christoph Schwarzbach:
An oversampling technique for the multiscale finite volume method to simulate electromagnetic responses in the frequency domain. CoRR abs/1610.02105 (2016) - [i3]Luz Angelica Caudillo-Mata, Eldad Haber, Lindsey J. Heagy, Christoph Schwarzbach:
A Framework for the Upscaling of the Electrical Conductivity in the Quasi-static Maxwell's Equations. CoRR abs/1610.02948 (2016) - 2015
- [c11]Justin Granek, Eldad Haber:
Data mining for real mining: A robust algorithm for prospectivity mapping with uncertainties. SDM 2015: 145-153 - [i2]Gili Rosenberg, Mohammad Vazifeh, Brad Woods, Eldad Haber:
Building an iterative heuristic solver for a quantum annealer. CoRR abs/1507.07605 (2015) - 2014
- [b1]Eldad Haber:
Computational Methods in Geophysical Electromagnetics. Mathematics in Industry, SIAM 2014, ISBN 978-1-611-97379-2, pp. I-IX, 1-144 - [j19]Jennifer Fohring, Eldad Haber, Lars Ruthotto:
Geophysical Imaging of Fluid Flow in Porous Media. SIAM J. Sci. Comput. 36(5) (2014) - [i1]Eldad Haber, Matthias Chung:
Simultaneous Source for non-uniform data variance and missing data. CoRR abs/1404.5254 (2014) - 2012
- [j18]Eldad Haber, Zhuojun Magnant, Christian Lucero, Luis Tenorio:
Numerical methods for A-optimal designs with a sparsity constraint for ill-posed inverse problems. Comput. Optim. Appl. 52(1): 293-314 (2012) - [j17]Matthias Chung, Eldad Haber:
Experimental Design for Biological Systems. SIAM J. Control. Optim. 50(1): 471-489 (2012) - [j16]Eldad Haber, Matthias Chung, Felix Herrmann:
An Effective Method for Parameter Estimation with PDE Constraints with Multiple Right-Hand Sides. SIAM J. Optim. 22(3): 739-757 (2012) - 2011
- [j15]Michele Benzi, Eldad Haber, Lauren Taralli:
A preconditioning technique for a class of PDE-constrained optimization problems. Adv. Comput. Math. 35(2-4): 149-173 (2011) - [j14]Eldad Haber, Scott P. MacLachlan:
A fast method for the solution of the Helmholtz equation. J. Comput. Phys. 230(12): 4403-4418 (2011) - [j13]Lior Horesh, Eldad Haber:
A Second Order Discretization of Maxwell's Equations in the Quasi-Static Regime on OcTree Grids. SIAM J. Sci. Comput. 33(5): 2805-2822 (2011) - 2010
- [j12]Eldad Haber, Raya Horesh, Jan Modersitzki:
Numerical optimization for constrained image registration. Numer. Linear Algebra Appl. 17(2-3): 343-359 (2010) - [j11]Steven P. Hamilton, Michele Benzi, Eldad Haber:
New multigrid smoothers for the Oseen problem. Numer. Linear Algebra Appl. 17(2-3): 557-576 (2010) - [j10]Eldad Haber, Tauseef ur Rehman, Allen R. Tannenbaum:
An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM J. Sci. Comput. 32(1): 197-211 (2010) - [c10]Ivan Kolesov, Peter Karasev, Allen R. Tannenbaum, Eldad Haber:
Fire and smoke detection in video with optimal mass transport based optical flow and neural networks. ICIP 2010: 761-764
2000 – 2009
- 2009
- [j9]Tauseef ur Rehman, Eldad Haber, Gallagher Pryor, John Melonakos, Allen R. Tannenbaum:
3D nonrigid registration via optimal mass transport on the GPU. Medical Image Anal. 13(6): 931-940 (2009) - [j8]Michele Benzi, Eldad Haber, Lauren Taralli:
Multilevel Algorithms for Large-Scale Interior Point Methods. SIAM J. Sci. Comput. 31(6): 4152-4175 (2009) - [c9]Eldad Haber, Stefan Heldmann, Jan Modersitzki:
A Scale-Space Approach to Landmark Constrained Image Registration. SSVM 2009: 612-623 - 2008
- [j7]Eldad Haber, Stefan Heldmann, Jan Modersitzki:
Adaptive Mesh Refinement for Nonparametric Image Registration. SIAM J. Sci. Comput. 30(6): 3012-3027 (2008) - [c8]Hanno Schumacher, Stefan Heldmann, Eldad Haber, Bernd Fischer:
Iterative Reconstruction of SPECT Images Using Adaptive Multi-level Refinement. Bildverarbeitung für die Medizin 2008: 318-322 - 2007
- [j6]Eldad Haber, Jan Modersitzki:
Image Registration with Guaranteed Displacement Regularity. Int. J. Comput. Vis. 71(3): 361-372 (2007) - [j5]Eldad Haber, Stefan Heldmann:
An octree multigrid method for quasi-static Maxwell's equations with highly discontinuous coefficients. J. Comput. Phys. 223(2): 783-796 (2007) - [j4]Eldad Haber, Stefan Heldmann, Jan Modersitzki:
An Octree Method for Parametric Image Registration. SIAM J. Sci. Comput. 29(5): 2008-2023 (2007) - 2006
- [j3]Eldad Haber, Jan Modersitzki:
A Multilevel Method for Image Registration. SIAM J. Sci. Comput. 27(5): 1594-1607 (2006) - [j2]Uri M. Ascher, Eldad Haber, Hui Huang:
On Effective Methods for Implicit Piecewise Smooth Surface Recovery. SIAM J. Sci. Comput. 28(1): 339-358 (2006) - [c7]William Gropp, Eldad Haber, Stefan Heldmann, David E. Keyes, Neill Miller, Jennifer M. Schopf, Tianzhi Yang:
Grid-based Image Registration. Grid-Based Problem Solving Environments 2006: 435-448 - [c6]Eldad Haber, Jan Modersitzki:
Intensity Gradient Based Registration and Fusion of Multi-modal Images. MICCAI (2) 2006: 726-733 - 2005
- [c5]Eldad Haber, Jan Modersitzki:
Beyond Mutual Information: A Simple and Robust Alternative. Bildverarbeitung für die Medizin 2005: 350-354 - [c4]Eldad Haber, Jan Modersitzki:
A Scale Space Method for Volume Preserving Image Registration. Scale-Space 2005: 561-572 - 2004
- [c3]Eldad Haber, Jan Modersitzki:
Volume Preserving Image Registration. MICCAI (1) 2004: 591-598 - 2003
- [c2]Uri M. Ascher, Eldad Haber:
Computational Methods for Large Distributed Parameter Estimation Problems in 3D. HPSC 2003: 15-36 - 2001
- [j1]Eldad Haber, Uri M. Ascher:
Fast Finite Volume Simulation of 3D Electromagnetic Problems with Highly Discontinuous Coefficients. SIAM J. Sci. Comput. 22(6): 1943-1961 (2001)
1990 – 1999
- 1998
- [c1]Eldad Haber:
SPECT image reconstruction using total variation. Image Processing 1998
Coauthor Index
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