Call for Submissions to the Resource-Constrained Machine Learning Workshop (ReCoML 2020)
The 1st workshop on Resource-Constrained Machine Learning will be co-located with MLSys this year (4th March 2020, Austin, Texas).
The ReCoML organizers will select papers based on a combination of novelty, quality, interest, and impact.
Topics of interest include, but are not limited to:
Compression of deep ML model architectures
Quantized and low-precision neural networks
Optimization of ML model architectures for resource-constrained environments
Hardware accelerators for deep ML models
Explainability of ML models in the context of resource-constrained environments
ML deployments over resource-constrained environments, e.g. Internet-of-Things (IoT) devices and edge-computing.
Reviewing process: All submissions should include the author’s names and their affiliations. The authors are allowed to post their paper on arXiv or other public forums. Key dates related to the reviewing process are given below:
Paper submission deadline: January 15, 2020
Decision notification: January 27, 2020
We invite research contributions in different formats:
Original research papers (up to 6 pages, not including references)
Position, opinion papers and extended abstracts (up to 4 pages, not including references)
Submission site: https://easychair.org/my/conference?conf=recoml20#
Dual submission policy: We will not accept any paper which, at the time of submission, is under review for another workshop or has already been published. This policy also applies to papers that overlap substantially in technical content with conference papers under review or previously published.
Proceedings: Accepted papers will be published in the form of online proceedings.
Submission format: To prepare your submission to ReCoML 2020, please use the LaTeX style files provided at ReCoML template.zip. Submitted papers will be in a 2-column format, each reference must explicitly list all the authors of the paper.