Conditional neural processes

Marta Gamelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, S. M.Ali Eslami

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function. On the other hand, Baycsian methods, such as Gaussian Processes (GPS), exploit prior knowledge to quickly infer the shape of a new function at test time. Yet GPS are computationally expensive, and it can be hard to design appropriate priors. In this paper we propose a family of neural models, Conditional Neural Processes (CNPs), that combine the benefits of both. CNPs arc inspired by the flexibility of stochastic processes such as GPS, but are structured as neural networks and trained via gradient descent. CNPs make accurate predictions after observing only a handful of training data points, yet scale to complex functions and large datasets. We demonstrate the performance and versatility of the approach on a range of canonical machine learning tasks, including regression, classification and image completion.

Original languageEnglish
Title of host publication35th International Conference on Machine Learning, ICML 2018
EditorsJennifer Dy, Andreas Krause
PublisherInternational Machine Learning Society (IMLS)
Pages2738-2747
Number of pages10
ISBN (Electronic)9781510867963
StatePublished - 2018
Externally publishedYes
Event35th International Conference on Machine Learning, ICML 2018 - Stockholm, Sweden
Duration: 10 Jul 201815 Jul 2018

Publication series

Name35th International Conference on Machine Learning, ICML 2018
Volume4

Conference

Conference35th International Conference on Machine Learning, ICML 2018
Country/TerritorySweden
CityStockholm
Period10/07/1815/07/18

Bibliographical note

Publisher Copyright:
© 2018 35th International Conference on Machine Learning, ICML 2018. All rights reserved.

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Human-Computer Interaction
  • Software

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