| A Banach spaces-based analysis of a new fully-mixed finite element method for the Boussinesq problem E Colmenares, GN Gatica, S Moraga ESAIM: Mathematical Modelling and Numerical Analysis 54 (5), 1525-1568, 2020 | 53 | 2020 |
| 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 | 42 | 2020 |
| 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 | 22 | 2022 |
| 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 |
| A fully-mixed finite element method for the steady state Oberbeck–Boussinesq system E Colmenares, GN Gatica, S Moraga, R Ruiz-Baier The SMAI Journal of computational mathematics 6, 125-157, 2020 | 12 | 2020 |
| 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 | 10 | 2022 |
| Optimal approximation of infinite-dimensional holomorphic functions B Adcock, N Dexter, S Moraga Calcolo 61 (1), 12, 2024 | 8 | 2024 |
| Optimal approximation of infinite-dimensional holomorphic functions B Adcock, N Dexter, S Moraga arXiv preprint arXiv:2305.18642, 2023 | 7 | 2023 |
| 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 | 4 | 2024 |
| 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 | 1 | 2024 |
| Effective deep neural network architectures for learning high-dimensional Banach-valued functions from limited data N Dexter, B Adcock, S Brugiapaglia, S Moraga 2022 Fall Southeastern Sectional Meeting. AMS, 2022 | 1 | 2022 |
| Optimal deep learning of holomorphic operators between Banach spaces B Adcock, N Dexter, S Moraga arXiv preprint arXiv:2406.13928, 2024 | | 2024 |
| Learning High-Dimensional Hilbert-Valued Functions With Deep Neural Networks From Limited Data. B Adcock, S Brugiapaglia, NC Dexter, S Moraga AAAI Spring Symposium: MLPS, 2021 | | 2021 |
| Centro de Investigación en Ingenierıa Matemática (CI 2 MA) E Colmenares, GN Gatica, S Moraga, R Ruiz-Baier | | |
| NEAR-OPTIMAL LEARNING OF BANACH-VALUED, HIGH-DIMENSIONAL FUNCTIONS VIA DEEP NEURAL NETWORKS FOR PARAMETRIC PDES S MORAGA, BEN ADCOCK, S BRUGIAPAGLIA, N DEXTER | | |
| The quest for optimal sampling strategies for learning sparse approximations in high dimensions JM Cardenas, N Dexter, S Moraga, B Adcock | | |
Nick DexterAssistant Professor, Department of Scientific Computing at Florida State University在 fsu.edu 的电子邮件经过验证
