Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Probabilistic Deep Learning with Probabilistic Neural Networks and Deep
  Probabilistic Models   Probabilistic deep learning is deep learning that accounts for uncertainty,
both model uncertainty and data uncertainty. It is based on the use of
probabilistic models and deep neural networks. We distinguish two approaches to
probabilistic deep learning: probabilistic neural networks and deep
probabilistic models. The former employs deep neural networks that utilize
probabilistic layers which can represent and process uncertainty; the latter
uses probabilistic models that incorporate deep neural network components which
capture complex non-linear stochastic relationships between the random
variables. We discuss some major examples of each approach including Bayesian
neural networks and mixture density networks (for probabilistic neural
networks), and variational autoencoders, deep Gaussian processes and deep mixed
effects models (for deep probabilistic models). TensorFlow Probability is a
library for probabilistic modeling and inference which can be used for both
approaches of probabilistic deep learning. We include its code examples for
illustration.

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The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
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The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: math
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The categories of this paper are: nucl-ex
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The categories of this paper are: supr-con
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The categories of this paper are: q-bio