Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Equivalence between Bayesian Priors and Penalties in Variational
  Inference   In machine learning, it is common to optimize the parameters of a
probabilistic model, modulated by an ad hoc regularization term that penalizes
some values of the parameters. Regularization terms appear naturally in
Variational Inference (VI), a tractable way to approximate Bayesian posteriors:
the loss to optimize contains a Kullback--Leibler divergence term between the
approximate posterior and a Bayesian prior. We fully characterize which
regularizers can arise this way, and provide a systematic way to compute the
corresponding prior. This viewpoint also provides a prediction for useful
values of the regularization factor in neural networks. We apply this framework
to regularizers such as L2, L1 or group-Lasso.

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The categories of this paper are: nucl-th
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The categories of this paper are: eess
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The categories of this paper are: physics
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The categories of this paper are: cond-mat
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin
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The categories of this paper are: econ
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The categories of this paper are: nucl-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: q-bio
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The categories of this paper are: astro-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
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The categories of this paper are: funct-an
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The categories of this paper are: gr-qc