Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tabular Benchmarks for Joint Architecture and Hyperparameter
  Optimization   Due to the high computational demands executing a rigorous comparison between
hyperparameter optimization (HPO) methods is often cumbersome. The goal of this
paper is to facilitate a better empirical evaluation of HPO methods by
providing benchmarks that are cheap to evaluate, but still represent realistic
use cases. We believe these benchmarks provide an easy and efficient way to
conduct reproducible experiments for neural hyperparameter search. Our
benchmarks consist of a large grid of configurations of a feed forward neural
network on four different regression datasets including architectural
hyperparameters and hyperparameters concerning the training pipeline. Based on
this data, we performed an in-depth analysis to gain a better understanding of
the properties of the optimization problem, as well as of the importance of
different types of hyperparameters. Second, we exhaustively compared various
different state-of-the-art methods from the hyperparameter optimization
literature on these benchmarks in terms of performance and robustness.

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The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
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The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an