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Embedded/Mobile DL Wkshp 2017 : 1st International Workshop on Embedded and Mobile Deep Learning (co-located with ACM MobiSys 2017)

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Link: http://www.cs.ucl.ac.uk/deepmobile_wkshp/index.html
 
When Jun 23, 2017 - Jun 23, 2017
Where NIAGARA FALLS, NY USA
Submission Deadline Apr 19, 2017
Notification Due Apr 26, 2017
Categories    deep learning   mobile computing   machine learning   embedded systems
 

Call For Papers

CFP: 1st International Workshop on Embedded and Mobile Deep Learning
WORKSHOP co-located with ACM MobiSys 2017
NIAGARA FALLS, NY USA - JUNE 2017

Submission Deadline: April 12th, 2017 – 11:59PM AOE

http://www.cs.ucl.ac.uk/deepmobile_wkshp/index.html

CALL FOR PAPERS

In recent years, breakthroughs from the field of deep learning have transformed how sensor data (e.g., images, audio, and even accelerometers and GPS) can be interpreted to extract the high-level information needed by bleeding-edge sensor-driven systems like smartphone apps and wearable devices. Today, the state-of-the-art in computational models that, for example, recognize a face, track user emotions, or monitor physical activities are increasingly based on deep learning principles and algorithms. Unfortunately, deep models typically exert severe demands on local device resources and this conventionally limits their adoption within mobile and embedded platforms. As a result, in far too many cases existing systems process sensor data with machine learning methods that have been superseded by deep learning years ago.
Because the robustness and quality of sensory perception and reasoning is so critical to mobile computing, it is critical for this community to begin the careful study of two core technical questions. First, how should deep learning learning principles and algorithms be applied to sensor inference problems that are central to this class of computing? This includes a combination of applications of learning some of which are familiar to other domains (such as the processing image and audio), in addition to those more uniquely tied to wearable and mobile systems (e.g., activity recognition). Second, what is required for current -- and future -- deep learning innovations to be either simplified or efficiently integrated into a variety of mobile resource-constrained systems? At heart, this MobiSys 2017 co-located workshop aims to consider these two broad themes; more specific topics of interest, include, but are not limited to:

= Compression of Deep Model Architectures
= Neural-based Approaches for Modeling User Activities and Behavior
= Quantized and Low-precision Neural Networks (including Binary Networks)
= Mobile Vision supported by Convolutional and Deep Networks
= Optimizing Commodity Processors (GPUs, DSPs etc.) for Deep Models
= Audio Analysis and Understanding through Recurrent and Deep Architectures
= Hardware Accelerators for Deep Neural Networks
= Distributed Deep Model Training Approaches
= Applications of Deep Neural Networks with Real-time Requirements
= Deep Models of Speech and Dialog Interaction or Mobile Devices
= Partitioned Networks for Improved Cloud- and Processor-Offloading
= Operating System Support for Resource Management at Inference-time

FULL PAPER SUBMISSIONS

Solicited submissions include: full technical workshop papers, descriptions of work-in-progress, and white position papers. Maximum length of such submissions is 6 pages, and if accepted they will be published by ACM and appear in the ACM Digital Library.

Submission Deadline: April 12th, 2017 – 11:59PM AOE
Author Notification: April 19th, 2017

POSTER/DEMO SUBMISSIONS

Poster/demo abstracts are also welcome and encouraged. Abstracts will be limited to 2 pages but will only be published on the workshop website (not the ACM DL). Deadlines for poster/demo submissions will remain open until the early registration deadline for the MobiSys 2017 conference closes.

WORKSHOP ORGANIZERS

PC Chairs
Nic Lane (University College London and Nokia Bell Labs)
Pete Warden (Google Brain)

PC Members
Sourav Bhattacharya (Nokia Bell Labs)
Petko Georgiev (Google Deep Mind)
Song Han (Stanford University)
Samir Kumar (Microsoft Ventures)
Robert LiKamWa (Arizona State University)
Youngki Lee (Singapore Management University)
Erran Li (Uber)
Laurens van der Maaten (Facebook AI Research)
Matthai Philipose (Microsoft Research)
Thomas Ploetz (Georgia Tech)
Heather Zheng (UC Santa Barbara)
Lin Zhong (Rice University)

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