Preprints

Matan Goren, Eran Treister, Physics-guided Full Waveform Inversion using Encoder-Solver Convolutional Neural Networks, under review, 2024, (arXiv).

Moshe Eliasof, Nir Ben-Zikri, Eran Treister. Rethinking Unsupervised Neural Superpixel Segmentation, submitted, 2023 (arXiv)

Sivan Sabato, Eran Treister, Elad Yom-Tov, Inferring Unfairness and Error from Population Statistics in Binary and Multiclass Classification, under review, 2022. (arXiv)

Tao Hong, Thanh-an Pham, Eran Treister, Michael Unser. Diffraction Tomography with Helmholtz Equation: Efficient and Robust Multigrid-Based Solver, 2021. (arXiv)

Journal and Conference Proceedings Papers

Sofya Feldman, Hadas Ner-Gaon, Eran Treister, Tal Shay, Assessing Application of Cross-Study Normalization Methods for Inter-Species Transcriptional Comparison, Accepted to PLoS ONE, 2024. (PONE)

Moshe Eliasof, Eldad Haber, Eran Treister, Every Node Counts: Improving the Training of Graph Neural Networks on Node Classification , Accepted to European Conference on Artificial Intelligence, 2024. (arXiv)(ECAI)

Shahaf E. Finder, Roy Amoyal, Eran Treister, Oren Freifeld, Wavelet Convolutions for Large Receptive Fields Accepted to European Conference on Computer Vision, 2024, (arXiv)(ECCV)

Moshe Eliasof, Eldad Haber, Eran Treister, An Over Complete Deep Learning Method for Inverse Problems, Accepted to Foundations of Data Science, 2024, (arXiv)(FoDS)

Moshe Eliasof, Eran Treister, Global-Local Graph Neural Networks for Node-Classification, Accepted to Pattern Recognition Letters, 2024, (arXiv)(PR letters)

Moshe Eliasof, Eldad Haber, Eran Treister, Graph Neural Reaction Diffusion Models, Accepted to SIAM Journal on Scientific Computing 2024, (arXiv)(SISC)

Bar Lerer, Ido Ben-Yair, Eran Treister, Multigrid-Augmented Deep Learning Preconditioners for the Helmholtz Equation using Compact Implicit Layers, SIAM Journal on Scientific Computing 2024, (arXiv)(SISC)

Ivan I. Kyrchei, Eran Treister, Volodymyr O. Pelykh, The Determinant of the Laplacian Matrix of a Quaternion Unit Gain Graph, Discrete Mathematics, 2024. (arXiv)(Disc. Math.)

Moshe Eliasof, Eldad Haber, Eran Treister, On The Temporal Domain of Differential Equation Inspired Graph Neural Networks, Accepted to Artificial Intelligence and Statistics 2024. (arXiv)(AISTATS)

Yakov Medvedovsky, Eran Treister, Tirza Routtenberg, Efficient Graph Laplacian Estimation by Proximal Newton, Accepted to Artificial Intelligence and Statistics 2024. (arXiv)(AISTATS)

Moshe Eliasof, Eldad Haber, Eran Treister, Feature Transportation Improves Graph Neural Networks, 38th AAAI Conference on Artificial Intelligence, 2024. (arXiv)(AAAI)

Rachel Yovel, Eran Treister, LFA-tuned matrix-free multigrid method for the elastic Helmholtz equation, SIAM Journal on Scientific Computing, 0, S1-S21, 2024, (arXiv)(SISC)

Moshe Eliasof, Eldad Haber, Eran Treister, DRIP: Deep Regularizers for Inverse Problems, Inverse Problems, 40, 1, 2024. (arXiv)(Inverse Problems)

Eran Treister and Rachel Yovel, A Hybrid Shifted Laplacian Multigrid and Domain Decomposition Preconditioner for the Elastic Helmholtz Equations, Journal of Computational Physics, 497, 2024, 112622. (arXiv)(JCP)

Gal Shalom, Eran Treister, Irad Yavneh, pISTA: preconditioned Iterative Soft Thresholding Algorithm for Graphical Lasso, SIAM Journal on Scientific Computing,0,0,S445--S466, 2023. (arXiv)(SISC)

Moshe Eliasof, Benjamin Bodner, Eran Treister, Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Neural Networks and Learning Systems, 35 (4), 4542-4553, 2024. (arXiv)(IEEE-TNNLS)

Moshe Eliasof, Fabrizio Frasca, Beatrice Bevilacqua, Eran Treister, Gal Chechik, Haggai Maron, Graph Positional Encoding via Random Feature Propagation, 40th International Conference on Machine Learning (ICML), 2023. (arXiv)(ICML)

Moshe Eliasof, Lars Ruthotto, Eran Treister, Improving Graph Neural Networks with Learnable Propagation Operators , 40th International Conference on Machine Learning (ICML), 2023. (arXiv)(ICML)

Maor Ashkenazi, Zohar Rimon, Ron Vainshtein, Shir Levi, Elad Richardson, Pinchas Mintz, Eran Treister, NeRN -- Learning Neural Representations for Neural Networks, The International Conference on Learning Representations (ICLR), 2023 (arXiv)(ICLR)

Moshe Eliasof, Jonathan Ephrath, Lars Ruthotto, Eran Treister. MGIC: Multigrid-in-Channels Neural Network Architectures, SIAM Journal on Scientific Computing, S307-S328, 2023. (arXiv)(SISC)

Shahaf Finder, Yair Zohav, Maor Ashkenazi, Eran Treister, Wavelet Feature Maps Compression for Image-to-Image CNNs, 36th Conference on Neural Information Processing Systems (NeurIPS), 2022. (arXiv)(NeurIPS)

Moshe Eliasof, Eldad Haber, Eran Treister, pathGCN: Learning General Graph Spatial Operators from Paths, 39th International Conference on Machine Learning (ICML), 2022. (arXiv)(ICML)

Ido Ben-Yair, Gil Ben Shalom, Moshe Eliasof, Eran Treister, Quantized convolutional neural networks through the lens of partial differential equations, Research in the Mathematical Sciences, 9, 58, 2022. (arXiv)(RMS)

Ruth Akiva Hochman, Shahaf E. Finder, Javier S. Turek, Eran Treister, Searching for N:M Fine-Grained Sparsity of Weights and Activations in Neural Networks. ECCV Workshop on Computational Aspects of Deep Learning (CADL), Oct 2022. (LNCS)

Benjamin J. Bodner, Gil Ben Shalom, Eran Treister. GradFreeBits: Gradient Free Bit Allocation for Mixed Precision Neural Networks, Sensors, 22(24), 9772 (Special issue on CNNs and Edge Computing Applications), 2022. (arXiv)(Sensors)

Yael Azulay and Eran Treister, Multigrid-augmented Deep Learning Preconditioners for the Helmholtz Equation, SIAM Journal on Scientific Computing, S127-S151, 2022. (arXiv)(SISC)

Moshe Eliasof, Tue Boesen, Eldad Haber, Chen Keasar, Eran Treister. Mimetic Neural Networks: A unified framework for Protein Design and Folding, Frontiers in Bioinformatics, 2, 2022. (arXiv)(Front. Bioinform.)

Shahaf E. Finder, Eran Treister, Oren Freifeld. Effective Learning of a GMRF Mixture Model, IEEE Access, 2022. (arXiv)(IEEE-Access)

Moshe Eliasof, Eldad Haber, Eran Treister.PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations, 35th Conference on Neural Information Processing Systems (NeurIPS), 2021. (arXiv)(NeurIPS)

Sagi Buchatsky and Eran Treister, Full waveform inversion using extended and simultaneous sources , SIAM Journal on Scientific Computing, 43,5, S862--S883, 2021. (arXiv)(SISC)

Moshe Eliasof, Eran Treister.DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling, 34th Conference on Neural Information Processing Systems (NeurIPS), 2020. (arXiv),(NeurIPS)

Jonathan Ephrath, Moshe Eliasof, Lars Ruthotto, Eldad Haber, Eran Treister. LeanConvNets: A Low-cost Yet Effective Convolutional Residual Networks, Journal of Selected Topics in Signal Processing, 14, 4, 894-904, 2020. (arXiv)(JSTSP)

Moshe Eliasof, Andrei Sharf, and Eran Treister, Multi-modal 3D Shape Reconstruction Under Calibration Uncertainty using Parametric Level Set Methods, SIAM J. Imaging Sciences, 13(1), 265–290, 2020. (arXiv)(SIIMS)

Eldad Haber, Keegan Lensink, Eran Treister, Lars Ruthotto. IMEXnet - A Forward Stable Deep Neural Network, 36th International Conference on Machine Learning (ICML), Long Beach, CA, USA, 2019. (arXiv)(ICML)

Eran Treister and Eldad Haber, A multigrid solver to the Helmholtz equation with a point source based on travel time and amplitude, Numerical Linear Algebra with Applications, 26 (1), e2206, 2019. (pdf) (NLAA)

Lars Ruthotto, Eran Treister and Eldad Haber, jInv--a flexible Julia package for PDE parameter estimation, SIAM Journal on Scientific Computing, 39 (5), S702-S722, 2017. (pdf)(SISC)

Eran Treister and Eldad Haber, Full waveform inversion guided by travel time tomography, SIAM Journal on Scientific Computing, 39 (5), S587-S609, 2017. (pdf)(SISC)

Eran Treister and Eldad Haber, A fast marching algorithm for the factored eikonal equation, Journal of Computational Physics, 324, 210-225, 2016. (pdf)(JCP)

Eran Treister, Javier Turek and Irad Yavneh, A Multilevel Framework for Sparse Optimization with Application to Inverse Covariance Estimation and Logistic Regression SIAM Journal on Scientific Computing, 38 (5), S566–S592, 2016. (pdf)(SISC)

Eran Treister and Irad Yavneh, Non-Galerkin Multigrid based on Sparsified Smoothed Aggregation. SIAM Journal on Scientific Computing, 37 (1), A30-A54, 2015. (pdf)(SISC)

Eran Treister and Javier Turek, A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation, Neural Information Processing Systems (NIPS), Dec. 2014. (pdf)(NIPS)

Eran Treister and Irad Yavneh, A Multilevel Iterated-Shrinkage Approach to l1 Penalized Least-Squares Minimization. IEEE Trans. on Signal Processing, 60, 12, 6319-6329, 2012. (pdf)(IEEE-TSP)

Hans De Sterck, Killian Miller, Eran Treister and Irad Yavneh, Fast Multilevel Methods for Markov-Chains. Numerical Linear Algebra with Applications, 18, 6, 961-980, 2011. (pdf)(NLAA)

Eran Treister and Irad Yavneh, On-the-fly Adaptive Smoothed Aggregation Multigrid for Markov-Chains. SIAM Journal on Scientific Computing, 33, 2927-2949, 2011.(pdf)(SISC)

Eran Treister and Irad Yavneh, Square and Stretch Multigrid for Stochastic Matrix Eigenproblems. Numerical Linear Algebra with Applications, 17, 229-251, 2010.(pdf)(NLAA)

Selected short conference and workshop papers and reports

Moshe Eliasof, Eldad Haber, Eran Treister, Advection Diffusion Reaction Graph Neural Networks for Spatio-Temporal Data, Learning on Graphs 2023, (LoG)

Moshe Eliasof, Nir Ben Zikri, Eran Treister, Rethinking Unsupervised Neural Superpixel Segmentation, IEEE International Conference on Image Processing (ICIP), 2022. (arXiv)

Benjamin J. Bodner, Gil Ben Shalom, Eran Treister. GradFreeBits: Gradient Free Bit Allocation for Dynamic Low Precision Neural Networks, IJCAI Workshop on Data Science Meets Optimization, 2021. (arXiv)

Jonathan Ephrath, Lars Ruthotto, Eldad Haber, Eran Treister. LeanResNet: A Low-cost yet Effective Convolutional Residual Networks, ICML Workshop on On-Device Machine Learning and Compact Deep Neural Network Representations (ODML-CDNNR), Long Beach, CA, USA, 2019. (arXiv)

Eran Treister, Lars Ruthotto, Michal Sharoni, Sapir Zafrani, and Eldad Haber, Low-Cost Parameterizations of Deep Convolution Neural Networks, ICSEE Symposium on Advances in Deep Learning, Eilat, Israel, 2018. (arXiv)

Eran Treister and Eldad Haber Joint Full Waveform Inversion and Travel Time Tomography. 78th EAGE Conference & Exhibition 2016. (EAGE)

Javier Turek and Eran Treister, A Multilevel Acceleration for l1-regularized Logistic Regression. Optimization Workshop at NIPS, Dec. 2015. (pdf)

Eran Treister, Javier Turek and Irad Yavneh, A Multilevel Framework for Sparse Inverse Covariance Estimation. Optimization Workshop at NIPS, Dec. 2014. (pdf)

Eran Treister, Aggregation-based Adaptive Algebraic Multigrid for Sparse Linear Systems. PhD Dissertation, Computer Science, Technion, 2014. Advisor: Prof. Irad Yavneh. (pdf)

Eran Treister, Ran Zemach and Irad Yavneh. Algebraic Collocation Coarse Approximation (ACCA) Multigrid. 12th Copper Mountain Conference on Iterative Methods, March 2012.(pdf)