Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data
  Principles   Machine learning (ML) is an increasingly important scientific tool supporting
decision making and knowledge generation in numerous fields. With this, it also
becomes more and more important that the results of ML experiments are
reproducible. Unfortunately, that often is not the case. Rather, ML, similar to
many other disciplines, faces a reproducibility crisis. In this paper, we
describe our goals and initial steps in supporting the end-to-end
reproducibility of ML pipelines. We investigate which factors beyond the
availability of source code and datasets influence reproducibility of ML
experiments. We propose ways to apply FAIR data practices to ML workflows. We
present our preliminary results on the role of our tool, ProvBook, in capturing
and comparing provenance of ML experiments and their reproducibility using
Jupyter Notebooks.

28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph