397 citations · 968 across the 32 of their papers we have counts for
5 papers · 1 filter
A Machine Learning Framework for Solving High-Dimensional Mean Field Game and Mean Field Control Problems
Lars Ruthotto, Stanley Osher, Wuchen Li +2
Mean field games (MFG) and mean field control (MFC) are critical classes of multi-agent models for efficient analysis of massive populations of interacting agents. Their areas of a…
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…
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…
ADMM-SOFTMAX : An ADMM Approach for Multinomial Logistic Regression
Samy Wu Fung, Sanna Tyrväinen, Lars Ruthotto +1
We present ADMM-Softmax, an alternating direction method of multipliers (ADMM) for solving multinomial logistic regression (MLR) problems. Our method is geared toward supervised cl…