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Sun, 8 Sep 2019 03:48:10 -0700
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CALL FOR PARTICIPATION: CML-IoT 2019

****** CML-IoT  2019 ******

 part of the UbiComp 2019

            London, UK

     September 9, 2019

https://cmliot2019.github.io/

********************************

This workshop aims to explore the intersection and combination of continual
machine learning and multimodal modeling with applications in Internet of
Things. The workshop welcomes works addressing these issues in different
applications and domains, such as human-centric sensing, smart cities,
health and wellness, privacy and security, etc. We aim at bringing together
researchers from different areas to establish a multidisciplinary community
and share the latest research.

Date: Monday, September 9, 2019

Time: 9:30 - 17:50

Room: Abbey

Keynote: The Deep (Learning) Transformation of Mobile and Embedded
Computing

Speaker: Nic Lane, University of Oxford & Samsung AI Center, Cambridge

Abstract: Mobile and embedded devices increasingly rely on deep neural
networks to understand the world -- a formerly impossible feat that would
have overwhelmed their system resources just a few years ago. The age of
on-device artificial intelligence is upon us; but incredibly, these
dramatic changes are just the beginning. Looking ahead, mobile machine
learning will extend beyond just classifying categories and perceptual
tasks, to roles that alter how every part of the systems stack of smart
devices function. This evolutionary step in constrained-resource computing
will finally produce devices that meet our expectations in how they can
learn, reason and react to the real-world. In this talk, I will briefly
discuss the initial breakthroughs that allowed us to reach this point, and
outline the next set of open problems we must overcome to bring about this
next deep transformation of mobile and embedded computing.



Presentations with 3 sessions:

   -

   Session 1: Adaptation in Mutimodal Learning
   -

   Session 2: Mutimodal Mobile Sensing
   -

   Session 3: Vision and Language


Presentation schedule: Detailed information about the schedule can be found
at https://cmliot2019.github.io/

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