Fetching the paper…
Reading the bibliography…
We propose deep convolutional Gaussian processes, a deep Gaussian process architecture with convolutional structure.
A practical bayesian framework for backpropagation networks
David JC MacKay · 1992
Earlier work this paper cites.
Computing with infinite networks
Christopher KI Williams · 1997
Earlier work this paper cites.
Gaussian processes in machine learning
Carl Edward Rasmussen · 2004
Earlier work this paper cites.
Gaussian process for machine learning
Carl Edward Rasmussen and Christopher KI Williams · 2006
Earlier work this paper cites.
Sparse gaussian processes using pseudo-inputs
Edward Snelson and Zoubin Ghahramani · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Additive gaussian processes
David K Duvenaud, Hannes Nickisch, and Carl E Rasmussen · 2011
Earlier work this paper cites.
Deep gaussian processes
Andreas Damianou and Neil Lawrence · 2013
Earlier work this paper cites.
Fast multidimensional pattern extrapolation with gaussian processes
A. Wilson, E. Gilboa, A. Nehorai, and J. Cunningham · 2013
Earlier work this paper cites.
Avoiding pathologies in very deep networks
David Duvenaud, Oren Rippel, Ryan Adams, and Zoubin Ghahramani · 2014
Earlier work this paper cites.
Adam: Amethod for stochastic optimization
Diederik P Kingma and Jimmy Lei Ba · 2014
Earlier work this paper cites.
Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Cited alongside, same era.
A complete recipe for stochastic gradient mcmc
Yi-An Ma, Tianqi Chen, and Emily Fox · 2015
Cited alongside, same era.
Kernel interpolation for scalable structured gaussian processes (kiss-gp)
Andrew Wilson and Hannes Nickisch · 2015
Cited alongside, same era.
Tensorflow: a system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Cited alongside, same era.
Deep learning , volume 1
Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio · 2016
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 · 2017
Later among the works it cites.
Non-stationary spectral kernels
Sami Remes, Markus Heinonen, and Samuel Kaski · 2017
Later among the works it cites.
Bayesian gan
Yunus Saatci and Andrew G Wilson · 2017
Later among the works it cites.
Doubly stochastic variational inference for deep gaussian processes
Hugh Salimbeni and Marc Deisenroth · 2017
Later among the works it cites.
Convolutional gaussian processes
Mark Van der Wilk, Carl Edward Rasmussen, and James Hensman · 2017
Later among the works it cites.
Scalable gaussian processes with grid-structured eigenfunctions (GP-GRIEF)
Trefor W Evans and Prasanth B Nair · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Understanding deep convolutional networks
Stéphane Mallat · 2016
Cited alongside, same era.
Variational inference: A review for statisticians
David M Blei, Alp Kucukelbir, and Jon D McAuliffe · 2017
Cited alongside, same era.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Autogp: Exploring the capabilities and limitations of Gaussian process models
Karl Krauth, Edwin V Bonilla, Kurt Cutajar, and Maurizio Filippone · 2017
Cited alongside, same era.
Deep neural networks as gaussian processes
Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S Schoenholz, Jeffrey Pennington, and Jascha Sohl-Dickstein · 2017
Cited alongside, same era.
Scalable variational gaussian process classification
James Hensman, Alexander G de G Matthews, and Zoubin Ghahramani
Cited in the paper.
Adrià Garriga-Alonso, Laurence Aitchison, and Carl Edward Rasmussen · 2018
Closest in time.
Inference in deep gaussian processes using stochastic gradient hamiltonian monte carlo
Marton Havasi, José Miguel Hernández Lobato, and Juan José Murillo Fuentes · 2018
Closest in time.
Deep gaussian processes with convolutional kernels
Vinayak Kumar, Vaibhav Singh, PK Srijith, and Andreas Damianou · 2018
Closest in time.
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
L. McInnes and J. Healy · 2018
Closest in time.
Differentiable compositional kernel learning for gaussian processes
S. Sun, G. Zhang, C. Wang, W. Zeng, J. Li, and R. Grosse · 2018
Closest in time.