Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Reproducible Deep Learning Workflows with DeepDIVA   The field of deep learning is experiencing a trend towards producing
reproducible research. Nevertheless, it is still often a frustrating experience
to reproduce scientific results. This is especially true in the machine
learning community, where it is considered acceptable to have black boxes in
your experiments. We present DeepDIVA, a framework designed to facilitate easy
experimentation and their reproduction. This framework allows researchers to
share their experiments with others, while providing functionality that allows
for easy experimentation, such as: boilerplate code, experiment management,
hyper-parameter optimization, verification of data integrity and visualization
of data and results. Additionally, the code of DeepDIVA is well-documented and
supported by several tutorials that allow a new user to quickly familiarize
themselves with the framework.

9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol