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2012.06081
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Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data
Mathematical and Scientific Machine Learning (MSML), 2020
11 December 2020
Ben Adcock
Simone Brugiapaglia
N. Dexter
S. Moraga
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Papers citing
"Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data"
21 / 21 papers shown
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A unified framework for learning with nonlinear model classes from arbitrary linear samples
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Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries
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Active Learning for Single Neuron Models with Lipschitz Non-Linearities
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Compressive Fourier collocation methods for high-dimensional diffusion equations with periodic boundary conditions
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On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samples
Memoirs of the European Mathematical Society (MMS), 2022
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Simone Brugiapaglia
N. Dexter
S. Moraga
237
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25 Mar 2022
A phase transition for finding needles in nonlinear haystacks with LASSO artificial neural networks
Statistics and computing (Stat. Comput.), 2022
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S. Sardy
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Yen Ting Lin
232
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Convergence Rates for Learning Linear Operators from Noisy Data
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Nikola B. Kovachki
Nicholas H. Nelsen
Andrew M. Stuart
445
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Neural Network Training Using
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Journal of Computational Physics (JCP), 2021
Subhayan De
Alireza Doostan
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The Random Feature Model for Input-Output Maps between Banach Spaces
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Andrew M. Stuart
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Numerical Solution of the Parametric Diffusion Equation by Deep Neural Networks
Journal of Scientific Computing (J. Sci. Comput.), 2020
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P. Petersen
Mones Raslan
R. Schneider
Gitta Kutyniok
255
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The troublesome kernel -- On hallucinations, no free lunches and the accuracy-stability trade-off in inverse problems
SIAM Review (SIAM Rev.), 2020
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Vegard Antun
A. Hansen
Ben Adcock
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05 Jan 2020
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