Simone Brugiapaglia
Simone Brugiapaglia
Assistant Professor, Concordia University, Department of Mathematics and Statistics
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Citata da
Citata da
Compressed sensing approaches for polynomial approximation of high-dimensional functions
B Adcock, S Brugiapaglia, CG Webster
Compressed Sensing and its Applications: Second International MATHEON …, 2017
Robustness to unknown error in sparse regularization
S Brugiapaglia, B Adcock
IEEE Transactions on Information Theory 64 (10), 6638-6661, 2018
Correcting for unknown errors in sparse high-dimensional function approximation
B Adcock, A Bao, S Brugiapaglia
Numerische Mathematik 142 (3), 667-711, 2019
A theoretical study of COmpRessed SolvING for advection-diffusion-reaction problems
S Brugiapaglia, F Nobile, S Micheletti, S Perotto
Mathematics of Computation 87 (309), 1-38, 2018
Compressed solving: A numerical approximation technique for elliptic PDEs based on Compressed Sensing
S Brugiapaglia, S Micheletti, S Perotto
Computers & Mathematics with Applications 70 (6), 1306-1335, 2015
On oracle-type local recovery guarantees in compressed sensing
B Adcock, C Boyer, S Brugiapaglia
Information and Inference: A Journal of the IMA 10 (1), 1-49, 2021
COmpRessed SolvING: sparse approximation of PDEs based on compressed sensing
S Brugiapaglia
Politecnico di Milano, 2016
Do log factors matter? On optimal wavelet approximation and the foundations of compressed sensing
B Adcock, S Brugiapaglia, M King–Roskamp
Foundations of Computational Mathematics, 1-61, 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
Sparse recovery in bounded Riesz systems with applications to numerical methods for PDEs
S Brugiapaglia, S Dirksen, HC Jung, H Rauhut
Applied and Computational Harmonic Analysis 53, 231-269, 2021
Compressive isogeometric analysis
S Brugiapaglia, L Tamellini, M Tani
Computers & Mathematics with Applications 80 (12), 3137-3155, 2020
Iterative and greedy algorithms for the sparsity in levels model in compressed sensing
B Adcock, S Brugiapaglia, M King-Roskamp
Wavelets and Sparsity XVIII 11138, 1113809, 2019
Wavelet–Fourier CORSING techniques for multidimensional advection–diffusion–reaction equations
S Brugiapaglia, S Micheletti, F Nobile, S Perotto
IMA Journal of Numerical Analysis 41 (4), 2744-2781, 2021
A compressive spectral collocation method for the diffusion equation under the restricted isometry property
S Brugiapaglia
Quantification of Uncertainty: Improving Efficiency and Technology, 15-40, 2020
Recovery guarantees for compressed sensing with unknown errors
S Brugiapaglia, B Adcock, RK Archibald
2017 International Conference on Sampling Theory and Applications (SampTA …, 2017
Generalizing Outside the Training Set: When Can Neural Networks Learn Identity Effects?
S Brugiapaglia, M Liu, P Tupper
arXiv preprint arXiv:2005.04330, 2020
Sparse approximation of multivariate functions from small datasets via weighted orthogonal matching pursuit
B Adcock, S Brugiapaglia
Spectral and High Order Methods for Partial Differential Equations, 611, 2020
The benefits of acting locally: Reconstruction algorithms for sparse in levels signals with stable and robust recovery guarantees
B Adcock, S Brugiapaglia, M King-Roskamp
IEEE Transactions on Signal Processing 69, 3160-3175, 2021
Invariance, encodings, and generalization: learning identity effects with neural networks
S Brugiapaglia, M Liu, P Tupper
arXiv preprint arXiv:2101.08386, 2021
Learning High-Dimensional Hilbert-Valued Functions With Deep Neural Networks From Limited Data
B Adcock, S Brugiapaglia, N Dexter, S Moraga
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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