BENELEARN 2017 INDUSTRY TRACK
The Industry Track is a special track within the Benelearn conference focussing on topics related to applications of machine learning, data mining and data science. A long history of machine learning research exists, but recently there has been yet another sudden surge in interest in machine learning applications within industry, the public sector, arts and the general public, as well as in societal aspects such as privacy, existential risk and using machine learning for the social good.
Topics of interest
We aim to attract researchers as well as practitioners, end users or policy makers with an interest in machine learning applications. Contributions could be case application studies, demonstrations, but also papers that more in general discuss topics that are relevant in the context of widespread application of machine learning, such as methodology, standards and platforms, ethical and societal aspects.
A non-exhaustive list of topics includes:
The conference solicits regular contributions (5-10 pages) of original work and extended abstracts (max 2 pages excl references) of either original work, or work that was recently accepted or published in a peer-reviewed machine-learning, data mining or other relevant journal or high level international conference. In the latter case, the publication reference should be clearly mentioned, and the abstracts will be checked mainly for relevance, rather than receive a full review.
Papers will be reviewed for relevance to the scope of the Industry Track, clarity, novelty, soundness, significance and impact. Note that technical or algorithmical depth and novelty is not strictly a requirement for the industry track given the goals and scope above, and we specifically are also open to non-technical papers, or application related papers that are not from industry, but from the arts, public sector, science or even people who just use machine learning for fun.
The program committee and track chairs will decide which contributions are selected for an oral presentation, and which ones are presented during a poster session (with spotlight presentations). If your submission falls under the general research track, or one of the special tracks on deep learning or complex networks, we encourage you to submit it there.
All submissions must be in English and must be submitted as PDF files through the EasyChair submission page.
Submissions should be formatted using the Benelearn 2017 LaTeX template. For detailed formatting instructions, please refer to the template files.
All accepted contributions will be published in the conference proceedings and be made publicly available on the Benelearn website, but copyright remains with the authors.