Kernel Mode Decomposition and programmable/interpretable regression
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Kernel Mode Decomposition and programmable/interpretable regression networks

19 July 2019

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Papers citing "Kernel Mode Decomposition and programmable/interpretable regression networks"

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Title
Learning dynamical systems from data: a simple cross-validation perspective B. Hamzi H. Owhadi 66 41 0 09 Jul 2020
Machine Learning of Linear Differential Equations using Gaussian Processes M. Raissi George Karniadakis 83 553 0 10 Jan 2017
A Probabilistic Framework for Deep Learning Ankit B. Patel M. T. Nguyen Richard G. Baraniuk BDL 115 68 0 06 Dec 2016
Variational Fourier features for Gaussian processes J. Hensman N. Durrande Arno Solin VLM 75 202 0 21 Nov 2016
Wave-shape function analysis -- when cepstrum meets time-frequency analysis Chen-Yun Lin Su Li Hau‐Tieng Wu 55 56 0 06 May 2016
Functional additive regression Yingying Fan Gareth M. James P. Radchenko 73 138 0 14 Oct 2015
Multigrid with rough coefficients and Multiresolution operator decomposition from Hierarchical Information Games H. Owhadi 62 169 0 11 Mar 2015
Scalable Variational Gaussian Process Classification J. Hensman A. G. Matthews Zoubin Ghahramani BDL 81 646 0 07 Nov 2014
Gaussian Processes for Big Data J. Hensman Nicolò Fusi Neil D. Lawrence GP 113 1,237 0 26 Sep 2013
Gaussian process models for periodicity detection N. Durrande J. Hensman M. Rattray Neil D. Lawrence 47 17 0 28 Mar 2013
Stochastic Variational Inference Matt Hoffman David M. Blei Chong-Jun Wang John Paisley BDL 277 2,628 0 29 Jun 2012
Additive Gaussian Processes David Duvenaud H. Nickisch C. Rasmussen GP 110 331 0 19 Dec 2011
Additive Covariance Kernels for High-Dimensional Gaussian Process Modeling N. Durrande D. Ginsbourger O. Roustant L. Carraro 96 102 0 27 Nov 2011
Kernels for Vector-Valued Functions: a Review Mauricio A. Alvarez Lorenzo Rosasco Neil D. Lawrence GP 218 930 0 30 Jun 2011
Additive Kernels for Gaussian Process Modeling N. Durrande D. Ginsbourger O. Roustant 94 25 0 21 Mar 2011