Suivre
Nick Dexter
Nick Dexter
Assistant Professor, Department of Scientific Computing at Florida State University
Adresse e-mail validée de fsu.edu - Page d'accueil
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Année
Obtaining genetics insights from deep learning via explainable artificial intelligence
G Novakovsky, N Dexter, MW Libbrecht, WW Wasserman, S Mostafavi
Nature Reviews Genetics 24 (2), 125-137, 2023
2562023
The gap between theory and practice in function approximation with deep neural networks
B Adcock, N Dexter
SIAM Journal on Mathematics of Data Science 3 (2), 624-655, 2021
1132021
Polynomial approximation via compressed sensing of high-dimensional functions on lower sets
A Chkifa, N Dexter, H Tran, C Webster
Mathematics of Computation 87 (311), 1415-1450, 2018
1032018
Deep neural networks are effective at learning high-dimensional Hilbert-valued functions from limited data
B Adcock, S Brugiapaglia, N Dexter, S Moraga
arXiv preprint arXiv:2012.06081, 2020
472020
Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks
B Adcock, S Brugiapaglia, N Dexter, S Moraga
Neural Networks 181, 106761, 2025
262025
A mixed ℓ1 regularization approach for sparse simultaneous approximation of parameterized PDEs
N Dexter, H Tran, C Webster
ESAIM: Mathematical Modelling and Numerical Analysis 53 (6), 2025-2045, 2019
212019
Towards optimal sampling for learning sparse approximations in high dimensions
B Adcock, JM Cardenas, N Dexter, S Moraga
High-Dimensional Optimization and Probability: With a View Towards Data …, 2022
152022
On Efficient Algorithms for Computing Near-Best Polynomial Approximations to High-Dimensional, Hilbert-Valued Functions from Limited Samples
B Adcock, S Brugiapaglia, N Dexter, S Moraga
Memoirs of the European Mathematical Society 13, 1-112, 2024
142024
Explicit cost bounds of stochastic Galerkin approximations for parameterized PDEs with random coefficients
NC Dexter, CG Webster, G Zhang
Computers & Mathematics with Applications 71 (11), 2231-2256, 2016
142016
Optimal approximation of infinite-dimensional holomorphic functions
B Adcock, N Dexter, S Moraga
Calcolo 61 (1), 12, 2024
132024
INGOT-DR: an interpretable classifier for predicting drug resistance in M. tuberculosis
H Zabeti, N Dexter, AH Safari, N Sedaghat, M Libbrecht, L Chindelevitch
Algorithms for Molecular Biology 16 (1), 17, 2021
112021
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions
JM Cardenas, B Adcock, N Dexter
Advances in Neural Information Processing Systems 36, 19990-20037, 2023
102023
Improved recovery guarantees and sampling strategies for TV minimization in compressive imaging
B Adcock, N Dexter, Q Xu
SIAM Journal on Imaging Sciences 14 (3), 1149-1183, 2021
102021
Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks
B Adcock, S Brugiapaglia, N Dexter, S Moraga
arXiv preprint arXiv:2404.03761, 2024
92024
CAS4DL: Christoffel adaptive sampling for function approximation via deep learning
B Adcock, JM Cardenas, N Dexter
Sampling Theory, Signal Processing, and Data Analysis 20 (2), 21, 2022
92022
An adaptive sampling and domain learning strategy for multivariate function approximation on unknown domains
B Adcock, JM Cardenas, N Dexter
SIAM Journal on Scientific Computing 45 (1), A200-A225, 2023
82023
On the strong convergence of forward-backward splitting in reconstructing jointly sparse signals
N Dexter, H Tran, CG Webster
Set-Valued and Variational Analysis 30 (2), 543-557, 2022
62022
Sparse reconstruction techniques for solutions of high-dimensional parametric PDEs
NC Dexter
42018
A unified framework for learning with nonlinear model classes from arbitrary linear samples
B Adcock, JM Cardenas, N Dexter
arXiv preprint arXiv:2311.14886, 2023
32023
Group testing large populations for SARS-CoV-2
H Zabeti, N Dexter, I Lau, L Unruh, B Adcock, L Chindelevitch
medRxiv, 2021.06. 03.21258258, 2021
32021
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