24 citations · 117 across the 44 of their papers we have counts for
4 papers · 1 filter
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…
Multi-modal 3D Shape Reconstruction Under Calibration Uncertainty using Parametric Level Set Methods
Moshe Eliasof, Andrei Sharf, Eran Treister
We consider the problem of 3D shape reconstruction from multi-modal data, given uncertain calibration parameters. Typically, 3D data modalities can be in diverse forms such as spar…
IMEXnet: A Forward Stable Deep Neural Network
Eldad Haber, Keegan Lensink, Eran Treister +1
Deep convolutional neural networks have revolutionized many machine learning and computer vision tasks, however, some remaining key challenges limit their wider use. These challeng…