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05/31/2023
Alternating Minimization for Regression with Tropical Rational Functions
We propose an alternating minimization heuristic for regression over the...
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03/08/2023
Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image Generation
Score-based diffusion models (SBDM) have recently emerged as state-of-th...
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10/31/2022
ωGNNs: Deep Graph Neural Networks Enhanced by Multiple Propagation Operators
Graph Neural Networks (GNNs) are limited in their propagation operators....
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02/23/2022
Multivariate Quantile Function Forecaster
We propose Multivariate Quantile Function Forecaster (MQF^2), a global p...
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09/28/2021
slimTrain – A Stochastic Approximation Method for Training Separable Deep Neural Networks
Deep neural networks (DNNs) have shown their success as high-dimensional...
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03/09/2021
An Introduction to Deep Generative Modeling
Deep generative models (DGM) are neural networks with many hidden layers...
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12/11/2020
Avoiding The Double Descent Phenomenon of Random Feature Models Using Hybrid Regularization
We demonstrate the ability of hybrid regularization methods to automatic...
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11/17/2020
Multigrid-in-Channels Neural Network Architectures
We present a multigrid-in-channels (MGIC) approach that tackles the quad...
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07/26/2020
Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection
Deep neural networks (DNNs) have achieved state-of-the-art performance a...
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06/11/2020
Multigrid-in-Channels Architectures for Wide Convolutional Neural Networks
We present a multigrid approach that combats the quadratic growth of the...
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05/29/2020
OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transport
A normalizing flow is an invertible mapping between an arbitrary probabi...
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05/27/2020
PNKH-B: A Projected Newton-Krylov Method for Large-Scale Bound-Constrained Optimization
We present PNKH-B, a projected Newton-Krylov method with a low-rank appr...
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05/27/2020
Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows
We compare the discretize-optimize (Disc-Opt) and optimize-discretize (O...
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02/15/2020
A Machine Learning Framework for Solving High-Dimensional Mean Field Game and Mean Field Control Problems
Mean field games (MFG) and mean field control (MFC) are critical classes...
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10/29/2019
LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have become indispensable for solvi...
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04/15/2019
LeanResNet: A Low-cost yet Effective Convolutional Residual Networks
Convolutional Neural Networks (CNNs) filter the input data using a serie...
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03/06/2019
IMEXnet: A Forward Stable Deep Neural Network
Deep convolutional neural networks have revolutionized many machine lear...
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01/27/2019
Large-Scale Classification using Multinomial Regression and ADMM
We present a novel method for learning the weights in multinomial logist...
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05/21/2018
Never look back - A modified EnKF method and its application to the training of neural networks without back propagation
In this work, we present a new derivative-free optimization method and i...
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05/21/2018
Never look back - The EnKF method and its application to the training of neural networks without back propagation
In this work, we present a new derivative-free optimization method and i...
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05/20/2018
Low-Cost Parameterizations of Deep Convolutional Neural Networks
Convolutional Neural Networks (CNNs) filter the input data using a serie...
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05/20/2018
Low-Cost Parameterizations of Deep Convolution Neural Networks
The main computational cost in the training of and prediction with Convo...
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04/12/2018
Deep Neural Networks motivated by Partial Differential Equations
Partial differential equations (PDEs) are indispensable for modeling man...
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03/07/2018
A Bayesian framework for molecular strain identification from mixed diagnostic samples
We provide a mathematical formulation and develop a computational framew...
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09/12/2017
Reversible Architectures for Arbitrarily Deep Residual Neural Networks
Recently, deep residual networks have been successfully applied in many ...
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05/28/2017
LAP: a Linearize and Project Method for Solving Inverse Problems with Coupled Variables
Many inverse problems involve two or more sets of variables that represe...
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05/09/2017
Stable Architectures for Deep Neural Networks
Deep neural networks have become invaluable tools for supervised machine...
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03/13/2017
A Lagrangian Gauss-Newton-Krylov Solver for Mass- and Intensity-Preserving Diffeomorphic Image Registration
We present an efficient solver for diffeomorphic image registration prob...
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03/06/2017
Learning across scales - A multiscale method for Convolution Neural Networks
In this work we establish the relation between optimal control and train...
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06/23/2016