CLASS-L Archives

April 2003

CLASS-L@LISTS.SUNYSB.EDU

Options: Use Monospaced Font
Show Text Part by Default
Show All Mail Headers

Message: [<< First] [< Prev] [Next >] [Last >>]
Topic: [<< First] [< Prev] [Next >] [Last >>]
Author: [<< First] [< Prev] [Next >] [Last >>]

Print Reply
Subject:
From:
Kiri Wagstaff <[log in to unmask]>
Reply To:
Classification, clustering, and phylogeny estimation
Date:
Wed, 2 Apr 2003 17:33:53 -0500
Content-Type:
TEXT/PLAIN
Parts/Attachments:
TEXT/PLAIN (63 lines)
----------------------------------------------------------------------
     Second Call for Papers and Participation: ICML-2003 Workshop
   Machine Learning Technologies for Autonomous Space Applications
             Thursday, August 21, 2003, Washington, D.C.
                   http://www.lunabots.com/icml2003/

                   Submission deadline: May 1, 2003

The ICML 2003 workshop on Machine Learning Technologies for Autonomous
Space Applications invites contributions from researchers and
practitioners in machine learning, space science, and mission
planning.  This workshop aims to bring together those interested in
developing novel machine learning algorithms for autonomous spacecraft
with those concerned with misson safety, performance, and engineering
constraints to bridge the "applicability divide".  Despite progress in
developing applicable ML techniques, adoption and integration into
fielded remote space missions remains a challenge.  The workshop will
provide a context for mission engineers and scientists to present
their "wish lists" and real-world constraints to machine learning
researchers and for ML scientists to present pertinent, cutting-edge
technologies.  The ultimate goal is to foster research and development
leading to the application of machine learning methods on real, flown
spacecraft.

We convene this workshop as a forum where we can address critical
questions such as:

* How can we design algorithms that can train for a long time under
  controlled situations, but must work almost perfectly in a remote,
  autonomous setting?
* How can ML techniques be tested so as to convince someone outside
  the field that they are reliable, robust, and effective for real
  space systems? What are the best analogue problems and situations,
  here on Earth, for the development and study of applicable ML
  techniques?
* Are there specific, possibly novel, metrics and methodologies for
  evaluation that would be most appropriate for these problems?
* What ML algorithms drawn from other domains (e.g., tasks with a high
  cost of failure) are applicable to the problems faced by fielded
  space missions?
* Can we provide formal performance guarantees for ML algorithms in
  the constrained and sometimes hostile environments in which remote
  space systems will exist?
* How can we strengthen connections between ML researchers and the
  people making operational decisions for space missions?

For a full description of the workshop focus and goals, visit the
website at http://www.lunabots.com/icml2003/ .  We also encourage you
to join the mailing list for announcements and discussion.  Send an
email to  [log in to unmask] with "subscribe icml2003-mlspace" in
the body.

Important Dates:
  May 1, 2003:    Technical submissions due
  May 25, 2003:   Notification of acceptance
  June 6, 2003:   Camera ready copies due
  August 1, 2003: Attendance-only submissions due

Chairs: Kiri Wagstaff (JHU/APL), Amy McGovern (UMass Amherst), and
Terran Lane (UNM)

----------------------------------------------------------------------

ATOM RSS1 RSS2