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SECOND CALL FOR CONTRIBUTIONS
Machine Learning for Assistive Technologies
a workshop in conjunction with
24th Annual Conference on Neural Information Processing Systems
(NIPS 2010)
December 10 2010 Whistler, BC, Canada
http://www.cs.uwaterloo.ca/~jhoey/mlat-nips2010
Deadline for Submissions: Wednesday, October 20, 2010
Notification of Decision: Wednesday, November 3, 2010
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Workshop Goals
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This workshop will expose the research area of assistive technology to
machine learning specialists, will provide a forum for machine
learning researchers and medical/industrial practitioners to
brainstorm about the main challenges, and will lead to developments of
new research ideas and directions in which machine learning approaches
are applied to complex assistive technology problems. The workshop
will discuss important open questions aimed at the next five years of
research in a number of key areas. More details follow below.
Invited Talks
------------------
Prasad Tadepalli, Oregon State University will speak from the machine
learning perspective
Matthai Philipose, Intel Corp, will speak from the industrial perspective
Overview:
---------------
This workshop will expose the research area of assistive technology to
machine learning specialists, will provide a forum for machine
learning researchers and medical/industrial practitioners to
brainstorm about the main challenges, and will lead to developments of
new research ideas and directions in which machine learning approaches
are applied to complex assistive technology problems. The workshop
will discuss important open questions aimed at the next five years of
research in a number of key areas, for example
1) What are the main bottlenecks that are currently holding back
complex assistive technologies from being widely deployed/used? The
argument to be presented and discussed at the workshop is that the
application of adaptivity and machine learning is one of these
bottlenecks. However, other viewpoints will be presented and
discussed.
2) Do assistive technologies need some new type of machine learning?
Are there any new machine learning problems or is it mostly a matter
of adapting existing machine learning techniques to assistive
technologies? A key challenge for assistive technologies is the
detection of novel or changing patterns of behavior. Are existing
novelty detection, feature selection and unsupervised learning
techniques sufficient to handle this challenge?
3) What are the bottlenecks for the scaling of machine learning
techniques for the assistive technology domain? More precisely, how
can ML algorithms scale to large domains both in terms of state,
action and observation spaces, and in terms of temporal extent?
Unsupervised learning, feature selection, distributivity, and
hierarchy are obvious choices. However, user adaptability and
customizability, the appropriate integration of prior knowledge, and
the rapid and inexpensive deployment of large sensor networks
(including cameras) also play a significant role.
Workshop Format
---------------
Participants will be machine learning specialists with an interest in
expanding their research profile into the area of assistive
technology, existing researchers in AT, practitioners in occupational
therapy with an interest in machine learning, and technology
developers with an interest in further developing their application
area into this novel field of research. The main focus of the workshop
will be on discussions and brainstorming sessions of breakout groups
with the explicit goal of identifying demands from the field of AT,
and ML related research topics that will help to overcome current
bottlenecks for successful AT approaches.
The workshop will consist of invited talks from two perspectives
(medical/industrial and academic/research) to be given by experts from
the field. Participants of the workshop will be asked to submit short
or long papers. Accepted papers will briefly be presented orally in
short (spotlight) sessions. Accompanying posters will be displayed
throughout the whole workshop. The workshop will then define breakout
discussion topics, and will allocate participants to groups for
brainstorming sessions, closing with presentations and discussions.
Significant time will be allocated to these breakout discussions and
the presentations of their findings.
Submissions:
-------------------
We welcome the following types of papers:
1. 6-8 page research papers that describe research in machine learning
as applied to assistive technology
2. 6-8 page research papers that describe studies of assistive
technology, emphasising the role (or potential role) of learning.
3. 2 page position statements or research abstacts from academia or
industry describing particular approaches or research techniques and
tools
Accepted papers will be presented as posters. Exceptional work will
be considered for oral presentation. Papers do not need to be blinded
and will be reviewed by the organising committee for suitability in the
workshop. Papers will be collected and distributed as workshop notes
(non-archival) at the conference. If the papers are of sufficient quantity
and quality, we will seek to publish them as an edited book or journal
special issue.
All submissions should adhere to NIPS format
(http://nips.cc/PaperInformation/StyleFiles). Please email your
submissions to: mlat.nips2...@gmail.com
Deadline for Submissions: Wednesday, October 20, 2010
Notification of Decision: Wednesday, November 3, 2010
Organizers:
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Jesse Hoey, University of Waterloo, jh...@cs.uwaterloo.ca
Pascal Poupart, University of Waterloo, ppoup...@cs.uwaterloo.ca
Thomas Plotz, Newcastle University, t.plo...@ncl.ac.uk
We look forward to receiving your submissions!
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