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no code implementations • 10 Oct 2021 • Moshe Eliasof, Benjamin Bodner, Eran Treister

Graph Convolutional Networks (GCNs) are widely used in a variety of applications, and can be seen as an unstructured version of standard Convolutional Neural Networks (CNNs).

no code implementations • NeurIPS Workshop DLDE 2021 • Yael Azulay, Eran Treister

We present a data-driven approach to iteratively solve the discrete heterogeneous Helmholtz equation at high wavenumbers.

no code implementations • NeurIPS Workshop DLDE 2021 • Ido Ben-Yair, Gil Ben Shalom, Moshe Eliasof, Eran Treister

Quantization of Convolutional Neural Networks (CNNs) is a common approach to ease the computational burden involved in the deployment of CNNs, especially on low-resource edge devices.

no code implementations • NeurIPS 2021 • Moshe Eliasof, Eldad Haber, Eran Treister

Moreover, as we demonstrate using an extensive set of experiments, our PDE-motivated networks can generalize and be effective for various types of problems from different fields.

no code implementations • 8 Jul 2021 • Tao Hong, Thanh-an Pham, Eran Treister, Michael Unser

In this work, we introduce instead a Helmholtz-based nonlinear model for inverse scattering.

no code implementations • 18 Feb 2021 • Benjamin J. Bodner, Gil Ben Shalom, Eran Treister

Quantized neural networks (QNNs) are among the main approaches for deploying deep neural networks on low resource edge devices.

no code implementations • 11 Feb 2021 • Sagi Buchatsky, Eran Treister

This way, we have a large-but-manageable additional parameter space, which has a rather low memory footprint, and is much more suitable for solving large scale instances of the problem than the full rank additional space.

Stochastic Optimization Computational Engineering, Finance, and Science Numerical Analysis Numerical Analysis 86A22, 86A15, 65M32, 65N22, 35Q86, 35R30

no code implementations • 7 Feb 2021 • Moshe Eliasof, Tue Boesen, Eldad Haber, Chen Keasar, Eran Treister

Recent advancements in machine learning techniques for protein folding motivate better results in its inverse problem -- protein design.

no code implementations • NeurIPS Workshop DLDE 2021 • Moshe Eliasof, Jonathan Ephrath, Lars Ruthotto, Eran Treister

We present a multigrid-in-channels (MGIC) approach that tackles the quadratic growth of the number of parameters with respect to the number of channels in standard convolutional neural networks (CNNs).

no code implementations • 11 Jun 2020 • Jonathan Ephrath, Lars Ruthotto, Eran Treister

We present a multigrid approach that combats the quadratic growth of the number of parameters with respect to the number of channels in standard convolutional neural networks (CNNs).

1 code implementation • NeurIPS 2020 • Moshe Eliasof, Eran Treister

Graph Convolutional Networks (GCNs) have shown to be effective in handling unordered data like point clouds and meshes.

1 code implementation • 18 May 2020 • Shahaf E. Finder, Eran Treister, Oren Freifeld

However, we show that even for a single Gaussian, when GLASSO is tuned to successfully estimate the sparsity pattern, it does so at the price of a substantial bias of the values of the nonzero entries of the matrix, and we show that this problem only worsens in a mixture setting.

no code implementations • 29 Oct 2019 • Jonathan Ephrath, Moshe Eliasof, Lars Ruthotto, Eldad Haber, Eran Treister

In practice, the input data and the hidden features consist of a large number of channels, which in most CNNs are fully coupled by the convolution operators.

2 code implementations • 23 Apr 2019 • Moshe Eliasof, Andrei Sharf, Eran Treister

This method not only allows us to analytically and compactly represent the object, it also confers on us the ability to overcome calibration related noise that originates from inaccurate acquisition parameters.

no code implementations • 15 Apr 2019 • Jonathan Ephrath, Lars Ruthotto, Eldad Haber, Eran Treister

Convolutional Neural Networks (CNNs) filter the input data using spatial convolution operators with compact stencils.

1 code implementation • 6 Mar 2019 • Eldad Haber, Keegan Lensink, Eran Treister, Lars Ruthotto

Deep convolutional neural networks have revolutionized many machine learning and computer vision tasks, however, some remaining key challenges limit their wider use.

no code implementations • 1 Jul 2016 • Eran Treister, Javier S. Turek, Irad Yavneh

A multilevel framework is presented for solving such l1 regularized sparse optimization problems efficiently.

3 code implementations • 23 Jun 2016 • Lars Ruthotto, Eran Treister, Eldad Haber

Estimating parameters of Partial Differential Equations (PDEs) from noisy and indirect measurements often requires solving ill-posed inverse problems.

Mathematical Software

no code implementations • NeurIPS 2014 • Eran Treister, Javier S. Turek

Numerical experiments on both synthetic and real gene expression data demonstrate that our approach outperforms the existing state of the art methods, especially for large-scale problems.

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