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 | 138 | 2023 |
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 | 94 | 2018 |
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 | 82 | 2021 |
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 | 35 | 2020 |
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 | 17 | 2019 |
Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks B Adcock, S Brugiapaglia, N Dexter, S Moraga arXiv preprint arXiv:2211.12633, 2022 | 15 | 2022 |
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 | 12 | 2021 |
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 | 12 | 2016 |
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 | 11 | 2022 |
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 arXiv preprint arXiv:2203.13908, 2022 | 11 | 2022 |
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 | 10 | 2021 |
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 | 6 | 2023 |
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 | 6 | 2022 |
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 | 6 | 2022 |
Optimal approximation of infinite-dimensional holomorphic functions B Adcock, N Dexter, S Moraga Calcolo 61 (1), 12, 2024 | 3 | 2024 |
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, 2024 | 3* | 2024 |
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 | 3 | 2021 |
Optimal approximation of infinite-dimensional holomorphic functions II: recovery from iid pointwise samples B Adcock, N Dexter, S Moraga arXiv preprint arXiv:2310.16940, 2023 | 2 | 2023 |
An interpretable classification method for predicting drug resistance in M. tuberculosis. bioRxiv 2020 H Zabeti, N Dexter, M Libbrecht preprint [https://doi. org/10.1101/2020.05. 31.115741], 2020 | 2 | 2020 |
Sparse reconstruction techniques for solutions of high-dimensional parametric PDEs NC Dexter | 2 | 2018 |