397 citations · 968 across the 32 of their papers we have counts for
3 papers · 1 filter
slimTrain -- A Stochastic Approximation Method for Training Separable Deep Neural Networks
Elizabeth Newman, Julianne Chung, Matthias Chung +1
Deep neural networks (DNNs) have shown their success as high-dimensional function approximators in many applications; however, training DNNs can be challenging in general. DNN trai…
An Introduction to Deep Generative Modeling
Lars Ruthotto, Eldad Haber
Deep generative models (DGM) are neural networks with many hidden layers trained to approximate complicated, high-dimensional probability distributions using a large number of samp…
A Neural Network Approach for High-Dimensional Optimal Control Applied to Multi-Agent Path Finding
Derek Onken, Levon Nurbekyan, Xingjian Li +3
We propose a neural network approach that yields approximate solutions for high-dimensional optimal control problems and demonstrate its effectiveness using examples from multi-age…