@inproceedings{0dfe24b4aa71497ba2fe25ee0771209a,
title = "Deep Convolutional Gaussian Processes",
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.",
keywords = "Convolutions, Gaussian processes, Variational inference",
author = "Kenneth Blomqvist and Samuel Kaski and Markus Heinonen",
year = "2020",
month = jan,
day = "1",
doi = "10.1007/978-3-030-46147-8_35",
language = "English",
isbn = "9783030461461",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "582--597",
editor = "Ulf Brefeld and Elisa Fromont and Andreas Hotho and Arno Knobbe and Marloes Maathuis and C{\'e}line Robardet",
booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings",
note = "European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD ; Conference date: 16-09-2019 Through 20-09-2019",
}