72 citations · 145 across the 7 of their papers we have counts for
10 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…
Avoiding The Double Descent Phenomenon of Random Feature Models Using Hybrid Regularization
Kelvin Kan, James G Nagy, Lars Ruthotto
We demonstrate the ability of hybrid regularization methods to automatically avoid the double descent phenomenon arising in the training of random feature models (RFM). The hallmar…
Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection
Elizabeth Newman, Lars Ruthotto, Joseph Hart +1
Deep neural networks (DNNs) have achieved state-of-the-art performance across a variety of traditional machine learning tasks, e.g., speech recognition, image classification, and s…
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…
Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows
Derek Onken, Lars Ruthotto
We compare the discretize-optimize (Disc-Opt) and optimize-discretize (Opt-Disc) approaches for time-series regression and continuous normalizing flows (CNFs) using neural ODEs. Ne…