15 citations · 19 across the 4 of their papers we have counts for
4 papers
MGIC: Multigrid-in-Channels Neural Network Architectures
Moshe Eliasof, Jonathan Ephrath, Lars Ruthotto +1
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 ne…
Multigrid-in-Channels Architectures for Wide Convolutional Neural Networks
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…
LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks
Jonathan Ephrath, Moshe Eliasof, Lars Ruthotto +2
Convolutional Neural Networks (CNNs) have become indispensable for solving machine learning tasks in speech recognition, computer vision, and other areas that involve high-dimensio…
LeanResNet: A Low-cost Yet Effective Convolutional Residual Networks
Jonathan Ephrath, Lars Ruthotto, Eldad Haber +1
Convolutional Neural Networks (CNNs) filter the input data using spatial convolution operators with compact stencils. Commonly, the convolution operators couple features from all c…