Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A statistical theory of cold posteriors in deep neural networks   To get Bayesian neural networks to perform comparably to standard neural
networks it is usually necessary to artificially reduce uncertainty using a
"tempered" or "cold" posterior. This is extremely concerning: if the prior is
accurate, Bayes inference/decision theory is optimal, and any artificial
changes to the posterior should harm performance. While this suggests that the
prior may be at fault, here we argue that in fact, BNNs for image
classification use the wrong likelihood. In particular, standard image
benchmark datasets such as CIFAR-10 are carefully curated. We develop a
generative model describing curation which gives a principled Bayesian account
of cold posteriors, because the likelihood under this new generative model
closely matches the tempered likelihoods used in past work.

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The categories of this paper are: cond-mat
2
The categories of this paper are: math
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
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The categories of this paper are: hep-ex
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex