This joint workshop aims to bring together researchers, educators, practitioners who are interested in techniques as well as applications of on-device machine learning and compact, efficient neural network representations. One aim of the workshop discussion is to establish close connection between researchers in the machine learning community and engineers in industry, and to benefit both academic researchers as well as industrial practitioners. The other aim is the evaluation and comparability of resource-efficient machine learning methods and compact and efficient network representations, and their relation to particular target platforms (some of which may be highly optimized for neural network inference). The research community has still to develop established evaluation procedures and metrics.
The workshop also aims at reproducibility and comparability of methods for compact and efficient neural network representations, and on-device machine learning. Contributors are thus encouraged to make their code available.
Topics of interest include, but are not limited to:
An extended abstract (3 pages long using ICML style, see https://icml.cc/Conferences/2019/StyleAuthorInstructions ) in PDF format should be submitted for evaluation of the originality and quality of the work. The evaluation is double-blind and the abstract must be anonymous. References may extend beyond the 3 page limit, and parallel submissions to a journal or conferences (e.g. AAAI or ICLR) are permitted.
Submissions will be accepted as contributed talks (oral) or poster presentations. Extended abstract should be submitted through EasyChair (https://easychair.org/my/conference.cgi?conf=odmlcdnnr2019). All accepted abstracts will be posted on the workshop website and archived.
Selection policy: all submitted abstracts will be evaluated based on their novelty, soundness and impacts. At the workshop we encourage DISCUSSION about NEW IDEAS.
* deadlines 23:59 anywhere on Earth (UTC-12)