Fetching the paper…
Reading the bibliography…
We present a practical way of introducing convolutional structure into Gaussian processes, making them more suited to high-dimensional inputs like images.
Functions of positive and negative type, and their connection with the theory of integral equations
J. Mercer · 1909
Earlier work this paper cites.
Learning deep architectures for ai
Yoshua Bengio · 1935
Earlier work this paper cites.
Approximation by superpositions of a sigmoidal function
G. Cybenko · 1989
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
Kurt Hornik · 1991
Earlier work this paper cites.
Bayesian learning for neural networks , volume 118
Radford M Neal · 1996
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
On the Influence of the Kernel on the Consistency of Support Vector Machines
Ingo Steinwart · 2001
Earlier work this paper cites.
On the Influence of the Kernel on the Consistency of Support Vector Machines
Ingo Steinwart · 2001
Earlier work this paper cites.
Fast forward selection to speed up sparse gaussian process regression
Matthias Seeger, Christopher K. I. Williams, and Neil D. Lawrence · 2003
Earlier work this paper cites.
Gaussian processes for machine learning
Matthias Seeger · 2004
Earlier work this paper cites.
Consistency of support vector machines and other regularized kernel classifiers
Ingo Steinwart · 2004
Earlier work this paper cites.
A unifying view of sparse approximate Gaussian process regression
Joaquin Quinonero-Candela and Carl Edward Rasmussen · 2005
Earlier work this paper cites.
Sparse Gaussian processes using pseudo-inputs
Edward Snelson and Zoubin Ghahramani · 2005
Earlier work this paper cites.
Sparse Gaussian processes using pseudo-inputs
Edward Snelson and Zoubin Ghahramani · 2005
Earlier work this paper cites.
Gaussian Processes for Machine Learning
Carl Edward Rasmussen and Christopher K.I. Williams · 2006
Earlier work this paper cites.
Gaussian Processes for Machine Learning
Carl Edward Rasmussen and Christopher K.I. Williams · 2006
Earlier work this paper cites.
Posterior consistency of gaussian process prior for nonparametric binary regression
Subhashis Ghosal and Anindya Roy · 2006
Earlier work this paper cites.
Universal kernels
Charles A. Micchelli, Yuesheng Xu, and Haizhang Zhang · 2006
Earlier work this paper cites.
Consistency and robustness of kernel-based regression in convex risk minimization
Andreas Christmann and Ingo Steinwart · 2007
Cited alongside, same era.
Group theoretical methods in machine learning
Risi Kondor · 2008
Cited alongside, same era.
Variational learning of inducing variables in sparse Gaussian processes
Michalis K Titsias · 2009
Cited alongside, same era.
The variational Gaussian approximation revisited
Manfred Opper and Cédric Archambeau · 2009
Cited alongside, same era.
Inter-domain Gaussian processes for sparse inference using inducing features
Anibal Figueiras-Vidal and Miguel Lázaro-Gredilla · 2009
Cited alongside, same era.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2009
Cited alongside, same era.
Convolutional kernel networks
Julien Mairal, Piotr Koniusz, Zaid Harchaoui, and Cordelia Schmid · 2014
Later among the works it cites.
Learning by stretching deep networks
Gaurav Pandey and Ambedkar Dukkipati · 2014
Later among the works it cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
Later among the works it cites.
Tree-structured Gaussian process approximations
Thang D Bui and Richard E Turner · 2014
Later among the works it cites.
Kernel interpolation for scalable structured Gaussian processes (KISS-GP)
Andrew Wilson and Hannes Nickisch · 2015
Later among the works it cites.
On degeneracy and invariances of random fields paths with applications in gaussian process modelling
David Ginsbourger, Olivier Roustant, and Nicolas Durrande · 2015
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Variational learning of inducing variables in sparse Gaussian processes
Michalis K Titsias · 2009
Cited alongside, same era.
Sparse spectrum Gaussian process regression
Miguel Lázaro-Gredilla, Joaquin Quiñonero-Candela, Carl Edward Rasmussen, and Aníbal R Figueiras-Vidal · 2010
Cited alongside, same era.
Sparse spectrum Gaussian process regression
Miguel Lázaro-Gredilla, Joaquin Quiñonero-Candela, Carl Edward Rasmussen, and Aníbal R Figueiras-Vidal · 2010
Cited alongside, same era.
Bayesian Nonparametric Models , pages 81–89
Peter Orbanz and Yee Whye Teh · 2010
Cited alongside, same era.
Additive gaussian processes
David K Duvenaud, Hannes Nickisch, and Carl E Rasmussen · 2011
Cited alongside, same era.
Universality, characteristic kernels and rkhs embedding of measures
Bharath K. Sriperumbudur, Kenji Fukumizu, and Gert R. G. Lanckriet · 2011
Cited alongside, same era.
Later among the works it cites.
On sparse variational methods and the Kullback-Leibler divergence between stochastic processes
Alexander G. de G. Matthews, James Hensman, Richard E Turner, and Zoubin Ghahramani · 2016
Later among the works it cites.
Stochastic variational deep kernel learning
Andrew G Wilson, Zhiting Hu, Ruslan R Salakhutdinov, and Eric P Xing · 2016
Later among the works it cites.
Manifold gaussian processes for regression
Roberto Calandra, Jan Peters, Carl Edward Rasmussen, and Marc Peter Deisenroth · 2016
Later among the works it cites.
Variational fourier features for gaussian processes
James Hensman, Nicolas Durrande, and Arno Solin · 2016
Later among the works it cites.
Scalable Gaussian Process Inference Using Variational Methods
Alexander G. de G. Matthews · 2016
Later among the works it cites.
Scalable gaussian process classification via expectation propagation
Daniel Hernández-Lobato and José Miguel Hernández-Lobato · 2016
Later among the works it cites.
A unifying framework for sparse gaussian process approximation using power expectation propagation
Thang D. Bui, Josiah Yan, and Richard E. Turner · 2016
Later among the works it cites.
Understanding probabilistic sparse gaussian process approximations
Matthias Stephan Bauer, Mark van der Wilk, and Carl Edward Rasmussen · 2016
Later among the works it cites.
GPflow: A Gaussian process library using TensorFlow
Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke. Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, and James Hensman · 2016
Later among the works it cites.
Autogp: Exploring the capabilities and limitations of gaussian process models, 2016
Karl Krauth, Edwin V. Bonilla, Kurt Cutajar, and Maurizio Filippone · 2016
Later among the works it cites.
Scalable multi-class Gaussian process classification using expectation propagation
Carlos Villacampa-Calvo and Daniel Hernández-Lobato · 2017
Closest in time.