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Conditional BRUNO: A neural process for exchangeable labelled data

Korshunova, I; Gal, Y; Gretton, A; Dambre, J; (2019) Conditional BRUNO: A neural process for exchangeable labelled data. In: ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. European Symposium on Artificial Neural Networks (ESANN): Bruges, Belgium. Green open access

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Abstract

We present a neural process that models exchangeable sequences of high-dimensional complex observations conditionally on a set of labels or tags. Our model combines the expressiveness of deep neural networks with the data-efficiency of Gaussian processes, resulting in a probabilistic model for which the posterior distribution is easy to evaluate and sample from, and the computational complexity scales linearly with the number of observations. The advantages of the proposed architecture are demonstrated on a challenging few-shot view reconstruction task which requires generalisation from short sequences of viewpoints.

Type: Proceedings paper
Title: Conditional BRUNO: A neural process for exchangeable labelled data
Event: 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
ISBN-13: 9782875870650
Open access status: An open access version is available from UCL Discovery
Publisher version: https://www.esann.org/node/19
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
URI: https://discovery.ucl.ac.uk/id/eprint/10084670
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