Neural Likelihoods for Multi-Output Gaussian Processes Article Swipe

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YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1905.13697

We construct flexible likelihoods for multi-output Gaussian process models that leverage neural networks as components. We make use of sparse variational inference methods to enable scalable approximate inference for the resulting class of models. An attractive feature of these models is that they can admit analytic predictive means even when the likelihood is non-linear and the predictive distributions are non-Gaussian. We validate the modeling potential of these models in a variety of experiments in both the supervised and unsupervised setting. We demonstrate that the flexibility of these `neural' likelihoods can improve prediction quality as compared to simpler Gaussian process models and that neural likelihoods can be readily combined with a variety of underlying Gaussian process models, including deep Gaussian processes.

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Gaussian Function

Computer Science

Artificial Intelligence

Physics

Quantum Mechanics

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Gaussian Computer science Gaussian process Artificial intelligence Pattern recognition (psychology) Physics Quantum mechanics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/1905.13697
PDF: https://arxiv.org/pdf/1905.13697
OA Status: green
Cited By: 2
References: 43
Related Works: 10
OpenAlex ID: https://openalex.org/W2947783661

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DOI: https://doi.org/10.48550/arxiv.1905.13697

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Title: Neural Likelihoods for Multi-Output Gaussian Processes

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Type: preprint

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Language: en

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Publication year: 2019

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Publication date: 2019-05-31

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Authors: Martin Jankowiak, Jacob R. Gardner

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Landing page: https://arxiv.org/abs/1905.13697

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PDF URL: https://arxiv.org/pdf/1905.13697

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Concepts: Gaussian, Computer science, Gaussian process, Artificial intelligence, Pattern recognition (psychology), Physics, Quantum mechanics

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Cited by: 2

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References (count): 43

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Related works (count): 10

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