| 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 | 255 | 2023 |
| 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 | 112 | 2021 |
| 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 | 102 | 2018 |
| 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 | 45 | 2020 |
| 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 | 25 | 2025 |
| 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 | 20 | 2019 |
| 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 | 14 | 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 Memoirs of the European Mathematical Society 13, 1-112, 2024 | 13 | 2024 |
| 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 | 13 | 2016 |
| Optimal approximation of infinite-dimensional holomorphic functions B Adcock, N Dexter, S Moraga Calcolo 61 (1), 12, 2024 | 12 | 2024 |
| 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 | 11 | 2021 |
| 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 | 10 | 2023 |
| 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 |
| 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 | 8 | 2024 |
| 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 | 8 | 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 | 8 | 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 |
| 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 | 3 | 2023 |
| 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 |
| Sparse reconstruction techniques for solutions of high-dimensional parametric PDEs NC Dexter | 3 | 2018 |
Ben AdcockProfessor, Simon Fraser University確認したメール アドレス: sfu.ca
