Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Sampling in Financial Statement Audits using Vector Quantised
  Autoencoder Neural Networks   The audit of financial statements is designed to collect reasonable assurance
that an issued statement is free from material misstatement 'true and fair
presentation'. International audit standards require the assessment of a
statements' underlying accounting relevant transactions referred to as 'journal
entries' to detect potential misstatements. To efficiently audit the increasing
quantities of such entries, auditors regularly conduct a sample-based
assessment referred to as 'audit sampling'. However, the task of audit sampling
is often conducted early in the overall audit process. Often at a stage, in
which an auditor might be unaware of all generative factors and their dynamics
that resulted in the journal entries in-scope of the audit. To overcome this
challenge, we propose the application of Vector Quantised-Variational
Autoencoder (VQ-VAE) neural networks. We demonstrate, based on two real-world
city payment datasets, that such artificial neural networks are capable of
learning a quantised representation of accounting data. We show that the
learned quantisation uncovers (i) the latent factors of variation and (ii) can
be utilised as a highly representative audit sample in financial statement
audits.

6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: alg-geom
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The categories of this paper are: plasm-ph
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The categories of this paper are: math
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The categories of this paper are: funct-an