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02/27/2023
An Approximation Theory Framework for Measure-Transport Sampling Algorithms
This article presents a general approximation-theoretic framework to ana...
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02/14/2023
Score-based Diffusion Models in Function Space
Diffusion models have recently emerged as a powerful framework for gener...
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08/27/2021
Convergence Rates for Learning Linear Operators from Noisy Data
We study the Bayesian inverse problem of learning a linear operator on a...
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05/07/2020
Model Reduction and Neural Networks for Parametric PDEs
We develop a general framework for data-driven approximation of input-ou...
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09/04/2019
Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning
Machine learning (ML) in the representation of molecular-orbital-based (...
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09/04/2019
Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-BasedMachine Learning
Machine learning (ML) in the representation of molecular-orbital-based (...
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06/10/2019
Analysis Of Momentum Methods
Gradient decent-based optimization methods underpin the parameter traini...
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08/10/2018
