Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Likelihoods for Multi-Output Gaussian Processes   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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The categories of this paper are: hep-ex
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
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The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
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The categories of this paper are: adap-org
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn
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The categories of this paper are: plasm-ph
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The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex