Call for Papers: Joint Workshop on On-Device Machine Learning &
Compact Deep Neural Network Representations (ODML-CDNNR)
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:
. Model compression for efficient inference with deep networks and
other ML models
. Learning efficient deep neural networks under memory and compute
constraints for on-device applications
. Low-precision training/inference & acceleration of deep neural
networks on mobile devices
. Sparsification, binarization, quantization, pruning, thresholding
and coding of neural network
. Deep neural network computation for low power consumption applications
. Efficient on-device ML for real-time applications in computer
vision, language understanding, speech processing, mobile health and
automotive (e.g., . computer vision for self-driving cars, video and
image compression), multimodal learning
. Software libraries (including open-source) optimized for efficient
inference and on-device ML
. Open datasets and test environments for benchmarking inference with
efficient DNN representations
. Metrics for evaluating the performance of efficient DNN representations
. Methods for comparing efficient DNN inference across platforms and tasks
Workshop Website:
https://ift.tt/2JH2jYW
Contact: icml2019-odml-cdnnr@googlegroups.com
Submission Instructions
An extended abstract (3 pages long using ICML style, see
https://ift.tt/2U7CANq ) 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://ift.tt/2JEhx0Z). 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.
Important Dates
Submission: Apr. 7, 2019
Notification: Apr. 24, 2019
Workshop: Jun. 14 or 15, 2019
Workshop organisers
Sujith Ravi, Google Research
Zornitsa Kozareva, Google
Lixin Fan, JD.com
Max Welling, Qualcomm & University of Amsterdam
Yurong Chen, Intel Labs China
Werner Bailer, Joanneum Research
Brian Kulis, Boston University
Haoji (Roland) Hu, Zhejiang University
Jonathan Dekhtiar, Nvidia
Yingyan Lin, Rice University
Diana Marculescu, Carnegie Mellon University