397 citations · 968 across the 32 of their papers we have counts for
7 papers · 1 filter
Gauss-Newton Optimization for Phase Recovery from the Bispectrum
James L. Herring, James Nagy, Lars Ruthotto
Phase recovery from the bispectrum is a central problem in speckle interferometry which can be posed as an optimization problem minimizing a weighted nonlinear least-squares object…
Layer-Parallel Training of Deep Residual Neural Networks
S. Günther, L. Ruthotto, J. B. Schroder +2
Residual neural networks (ResNets) are a promising class of deep neural networks that have shown excellent performance for a number of learning tasks, e.g., image classification an…
An Uncertainty-Weighted Asynchronous ADMM Method for Parallel PDE Parameter Estimation
Samy Wu Fung, Lars Ruthotto
We consider a global variable consensus ADMM algorithm for solving large-scale PDE parameter estimation problems asynchronously and in parallel. To this end, we partition the data…
Never look back - A modified EnKF method and its application to the training of neural networks without back propagation
Eldad Haber, Felix Lucka, Lars Ruthotto
In this work, we present a new derivative-free optimization method and investigate its use for training neural networks. Our method is motivated by the Ensemble Kalman Filter (EnKF…
Low-Cost Parameterizations of Deep Convolutional Neural Networks
Eran Treister, Lars Ruthotto, Michal Sharoni +2
Convolutional Neural Networks (CNNs) filter the input data using a series of spatial convolution operators with compactly supported stencils and point-wise nonlinearities. Commonly…
Deep Neural Networks Motivated by Partial Differential Equations
Lars Ruthotto, Eldad Haber
Partial differential equations (PDEs) are indispensable for modeling many physical phenomena and also commonly used for solving image processing tasks. In the latter area, PDE-base…