Search Results for author: Jonathan Ephrath

Found 4 papers, 1 papers with code

MGIC: Multigrid-in-Channels Neural Network Architectures

1 code implementation 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).

Image Classification Point Cloud Classification

Multigrid-in-Channels Architectures for Wide Convolutional Neural Networks

no code implementations11 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).

Image Classification

LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks

no code implementations29 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.

Image Classification Semantic Segmentation +2

LeanResNet: A Low-cost Yet Effective Convolutional Residual Networks

no code implementations15 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.

General Classification Image Classification

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