Sparse Spectral Approximations
Sparse Spectral Approximations is a computational framework within spectral methods that leverages sparsity-promoting techniques to efficiently approximate eigenfunctions or spectral representations of differential operators while retaining high accuracy with reduced degrees of freedom.
132
Publications
9.9K
Citations
304
Authors
163
Institutions
Publications per year
2017–2026
27
Parents
132
Graph Sparsification by Effective Resistances
Daniel A. Spielman, Nikhil Srivastava · SIAM Journal on Computing · 2011 · 504 citations
Sparse Spectrum Gaussian Process Regression
Miguel Lázaro-Gredilla, Joaquin Quiñonero-Candela, Carl Edward Rasmussen et al. · 2010 · 390 citations
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163
Princeton University
Princeton, United States
5 h-index
9 publications
7.7K citations
Massachusetts Institute of Technology
Cambridge, United States
5 h-index
9 publications
424 citations

Centre National de la Recherche Scientifique
Paris, France
5 h-index
10 publications
315 citations
University of California, Berkeley
Berkeley, United States
4 h-index
6 publications
238 citations
Yale University
New Haven, United States
4 h-index
8 publications
1.4K citations
University of Cambridge
Cambridge, United Kingdom
4 h-index
8 publications
751 citations
Purdue University West Lafayette
West Lafayette, United States
3 h-index
5 publications
452 citations

University of Göttingen
Göttingen, Germany
3 h-index
4 publications
62 citations
1–8 of 163
Repository · 6 publications · 147 citations
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6 publications · 127 citations
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4 publications · 230 citations
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4 publications · 190 citations
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3 publications · 189 citations
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