Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning for Bayesian Optimization of Scientific Problems with
  High-Dimensional Structure   Bayesian optimization (BO) is a popular paradigm for global optimization of
expensive black-box functions, but there are many domains where the function is
not completely black-box. The data may have some known structure (e.g.
symmetries) and/or the data generation process can yield useful intermediate or
auxiliary information in addition to the value of the optimization objective.
However, surrogate models traditionally employed in BO, such as Gaussian
Processes (GPs), scale poorly with dataset size and do not easily accommodate
known structure or auxiliary information. Instead, we propose performing BO on
complex, structured problems by using deep learning models with uncertainty, a
class of scalable surrogate models that have the representation power and
flexibility to handle structured data and exploit auxiliary information. We
demonstrate BO on a number of realistic problems in physics and chemistry,
including topology optimization of photonic crystal materials using
convolutional neural networks, and chemical property optimization of molecules
using graph neural networks. On these complex tasks, we show that neural
networks often outperform GPs as surrogate models for BO in terms of both
sampling efficiency and computational cost.

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The categories of this paper are: nucl-th
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The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
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The categories of this paper are: q-fin
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The categories of this paper are: eess
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The categories of this paper are: atom-ph
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The categories of this paper are: nlin
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph
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The categories of this paper are: supr-con
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The categories of this paper are: comp-gas
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The categories of this paper are: astro-ph