Dear list owners,
I would like to submit the attached CFP for distribution on the Inductive
list. Thank you!
Kiri
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------ Kiri Wagstaff, Ph.D. ------ [EMAIL PROTECTED] ------
Senior Researcher at the Jet Propulsion Laboratory
Machine Learning Systems Group: http://ml.jpl.nasa.gov/
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Machine Learning in Space: Extending Our Reach
Special Issue of the Machine Learning Journal
Amy McGovern and Kiri L. Wagstaff, guest editors
URL: http://www.wkiri.com/ml4space
Submission deadline: July 1, 2007
Remote space environments simultaneously present significant challenges to the
machine learning community and enormous opportunities for advancement.
Enhancing spacecraft autonomy with machine learning has the potential to permit
new discoveries that pre-scripted activities would preclude. On-board machine
learning could enable intelligent filtering or prioritizing of data as it is
collected to make the best use of the available bandwidth. Rovers with
learning capabilities could more thoroughly and more quickly explore new
environments, relating them to previously observed areas and highlighting novel
or unexpected observations. While some initial tests have been made in this
direction, the increasing computational power now available on spacecraft has
broadened the field of what could feasibly be done on-board. Ultimately,
machine learning can help these spacecraft graduate from their current status
as "science prosthetics" into "science assistants".
The purpose of this special issue is to collect recent advances in machine
learning for remote space or planetary environments and to identify novel space
applications where machine learning could significantly increase capabilities,
robustness, and/or efficiency.
Key topics of interest include:
- How to perform machine learning in a high-risk, remote environment
- Learning with resource constraints (memory, computation, etc.)
- Multi-instrument machine learning
- Multi-mission machine learning
- Novel applications and uses of machine learning in space
- How to evaluate and validate machine learning methods prior to
deployment on-board a spacecraft
- Methods for safe real-time learning
- Methods that trade off exploration and exploitation, given mission
science goals and safety/reliability requirements
- Methods for reducing risk and increasing acceptance of machine
learning in space flight missions
- A survey of space-borne machine learning accomplishments
We encourage all prospective authors to email us with a brief summary of the
paper concept for feedback, especially for survey papers or papers focused on
applications.
Submissions are expected to represent high-quality, significant contributions
in the area of machine learning algorithms and/or applications. Authors should
follow standard formatting guidelines for Machine Learning manuscripts.
Administrative notes:
* Authors retain the copyrights to their papers. (See publication
agreement on the MLJ website:
http://pages.stern.nyu.edu/~fprovost/MLJ/.)
* Submissions and reviewing will be handled electronically using
standard procedures for Machine Learning (http://mach.edmgr.com).
* Authors must register with the system before they can submit their
manuscripts.
* Authors must select the appropriate Article Type -- Machine
Learning in Space -- when submitting their manuscripts.
* Accepted papers will be published electronically and citable
immediately (before the print version appears).
Schedule
Submission Deadline: July 1, 2007
Send Papers to Reviewers: July 15, 2007
Reviews Due Back to Editors: September 1, 2007
Decisions Announced: September 15, 2007
Camera-Ready Due: October 31, 2007
Print Publication: Early 2008
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