Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Review of the Gumbel-max Trick and its Extensions for Discrete
  Stochasticity in Machine Learning   The Gumbel-max trick is a method to draw a sample from a categorical
distribution, given by its unnormalized (log-)probabilities. Over the past
years, the machine learning community has proposed several extensions of this
trick to facilitate, e.g., drawing multiple samples, sampling from structured
domains, or gradient estimation for error backpropagation in neural network
optimization. The goal of this survey article is to present background about
the Gumbel-max trick, and to provide a structured overview of its extensions to
ease algorithm selection. Moreover, it presents a comprehensive outline of
(machine learning) literature in which Gumbel-based algorithms have been
leveraged, reviews commonly-made design choices, and sketches a future
perspective.

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The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom
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The categories of this paper are: eess
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The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
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The categories of this paper are: supr-con
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The categories of this paper are: q-fin
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The categories of this paper are: cond-mat
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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: gr-qc
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The categories of this paper are: chao-dyn