Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Hybrid Bootstrap: A Drop-in Replacement for Dropout   Regularization is an important component of predictive model building. The
hybrid bootstrap is a regularization technique that functions similarly to
dropout except that features are resampled from other training points rather
than replaced with zeros. We show that the hybrid bootstrap offers superior
performance to dropout. We also present a sampling based technique to simplify
hyperparameter choice. Next, we provide an alternative sampling technique for
convolutional neural networks. Finally, we demonstrate the efficacy of the
hybrid bootstrap on non-image tasks using tree-based models.

35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph