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BEAT: An Open-Science Web Platform
Type of publication: Conference paper
Citation: Anjos_ICML2017_2017
Publication status: Accepted
Booktitle: Thirty-fourth International Conference on Machine Learning
Year: 2017
Month: August
Location: Sydney, Australia
Note: https://openreview.net/group?id=ICML.cc/2017/RML
URL: https://beat-eu.org/platform...
Abstract: With the increased interest in computational sciences, machine learning (ML), pattern recognition (PR) and big data, governmental agencies, academia and manufacturers are overwhelmed by the constant influx of new algorithms and techniques promising improved performance, generalization and robustness. Sadly, result reproducibility is often an overlooked feature accompanying original research publications, competitions and benchmark evaluations. The main reasons behind such a gap arise from natural complications in research and development in this area: the distribution of data may be a sensitive issue; software frameworks are difficult to install and maintain; Test protocols may involve a potentially large set of intricate steps which are difficult to handle. To bridge this gap, we built an open platform for research in computational sciences related to pattern recognition and machine learning, to help on the development, reproducibility and certification of results obtained in the field. By making use of such a system, academic, governmental or industrial organizations enable users to easily and socially develop processing toolchains, re-use data, algorithms, workflows and compare results from distinct algorithms and/or parameterizations with minimal effort. This article presents such a platform and discusses some of its key features, uses and limitations. We overview a currently operational prototype and provide design insights.
Projects BEAT
Authors Anjos, André
El Shafey, Laurent
Marcel, Sébastien
Added by: [UNK]
Total mark: 0
  • Anjos_ICML2017_2017.pdf