Abstract

We propose deep convolutional Gaussian processes, a deep Gaussian process architecture with convolutional structure. The model is a principled Bayesian framework for detecting hierarchical combinations of local features for image classification. We demonstrate greatly improved image classification performance compared to current convolutional Gaussian process approaches on the MNIST and CIFAR-10 datasets. In particular, we improve state-of-the-art CIFAR-10 accuracy by over 10% points.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings
EditorsUlf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes Maathuis, Céline Robardet
PublisherSPRINGER
Pages582-597
Number of pages16
ISBN (Print)9783030461461
DOIs
Publication statusPublished - 1 Jan 2020
MoE publication typeA4 Article in a conference publication
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - Wurzburg, Germany
Duration: 16 Sep 201920 Sep 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11907 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Abbreviated titleECML PKDD
CountryGermany
CityWurzburg
Period16/09/201920/09/2019

Keywords

  • Convolutions
  • Gaussian processes
  • Variational inference

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  • Cite this

    Blomqvist, K., Kaski, S., & Heinonen, M. (2020). Deep Convolutional Gaussian Processes. In U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, & C. Robardet (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings (pp. 582-597). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11907 LNAI). SPRINGER . https://doi.org/10.1007/978-3-030-46147-8_35