metadata-draft.yaml

# To be filled by the author(s) at the time of submission
# -------------------------------------------------------

# Title of the article:
#  - For a successful replication, it shoudl be prefixed with "[Re]"
#  - For a failed replication, it should be prefixed with "[¬Re]"
#  - For other article types, no instruction (but please, not too long)
title: "[Re] Meta-learning with differentiable closed-form solvers"

# List of authors with name, orcid number, email and affiliation
# Affiliation "*" means contact author
authors:
  - name: Arnout Devos
    orcid: 0000-0002-9425-1163
    email: arnout.devos@epfl.ch
    affiliations: 1,2,*

  - name: Sylvain Chatel
    email: sylvain.chatel@epfl.ch
    affiliations: 1,2     # * is for contact author

  - name: Matthias Grossglauser
    email: matthias.grossglauser@epfl.ch
    affiliations: 1

# List of affiliations with code (corresponding to author affiliations), name
# and address. You can also use these affiliations to add text such as "Equal
# contributions" as name (with no address).
affiliations:
  - code:    1
    name:    Swiss Federal Institute of Technology Lausanne (EPFL)
    address: Switzerland
  - code:    2
    name:    Equal contribution

# List of keywords (adding the programming language might be a good idea)
keywords: rescience c, rescience x, Machine Learning, Meta-Learning, Few-Shot, Deep-Learning, Python

# Code URL and DOI (url is mandatory for replication, doi after acceptance)
# You can get a DOI for your code from Zenodo,
#   see https://guides.github.com/activities/citable-code/
code:
  - url: https://github.com/ArnoutDevos/r2d2
  - doi:

# Date URL and DOI (optional if no data)
data:
  - url:
  - doi:

# Information about the original article that has been replicated
replication:
 - cite: # Full textual citation
 - bib:  # Bibtex key (if any) in your bibliography file
 - url:  # URL to the PDF, try to link to a non-paywall version
 - doi:  # Regular digital object identifier

# Don't forget to surround abstract with double quotes
abstract: "In this paper, we present a reproduction of the paper of
          Bertinetto et al. [1] ”Meta- learning with differentiable closed-form
          solvers” as part of the ICLR 2019 Reproducibility Challenge. In
          successfully reproducing the most crucial part of the paper, we reach
          a performance that is comparable with or superior to the original
          paper on two benchmarks for several settings. We evaluate new baseline
          results, using a new dataset presented in the paper. Yet, we also
          provide multiple remarks and recommendations about reproducibility and
          comparability. After we brought our reproducibility work to the
          authorsʼ attention, they have updated the original paper on which this
          work is based and released code as well. Our contributions mainly
          consist in reproducing the most important results of their original
          paper, in giving insight in the reproducibility and in providing a
          first open-source implementation."

# Bibliography file (yours)
bibliography: bibliography.bib

# Type of the article
# Type can be:
#  * Editorial
#  * Letter
#  * Replication
type: Replication

# Scientific domain of the article (e.g. Computational Neuroscience)
#  (one domain only & try to be not overly specific)
domain: Machine Learning

# Coding language (main one only if several)
language: Python


# To be filled by the author(s) after acceptance
# -----------------------------------------------------------------------------

# For example, the URL of the GitHub issue where review actually occured
review:
  - url:

contributors:
  - name:
    orcid:
    role: editor
  - name:
    orcid:
    role: reviewer
  - name:
    orcid:
    role: reviewer

# This information will be provided by the editor
dates:
  - received:  November 1, 2018
  - accepted:
  - published:

# This information will be provided by the editor
article:
  - number: # Article number will be automatically assigned during publication
  - doi:    # DOI from Zenodo
  - url:    # Final PDF URL (Zenodo or rescience website?)

# This information will be provided by the editor
journal:
  - name:   "ReScience C"
  - issn:   2430-3658
  - volume: 4
  - issue:  1