-- Apologies if you receive multiple copies of this announcement --
------------------------------------------------------------
CALL FOR CONTRIBUTIONS
NIPS 2010 workshop on Transfer Learning Via
Rich Generative Models.
Whistler, BC, Canada, December 11, 2010
http://www.mit.edu/~rsalakhu/workshop_nips2010/index.html
Important Dates:
----------------
Deadline for submissions: October 20, 2010
Notification of acceptance: October 27, 2010
Overview:
----------------
Intelligent systems must be capable of transferring previously-learned abstract
knowledge to new concepts, given only a small or noisy set of examples. This
transfer of higher order information to new learning tasks lies at the core of
many problems in the fields of computer vision, cognitive science, machine
learning, speech perception and natural language processing.
Over the last decade, there has been considerable progress in developing
cross-task transfer (e.g., multi-task learning and semi-supervised learning)
using both discriminative and generative approaches. However, many existing
learning systems today can not cope with new tasks for which they have not been
specifically trained. Even when applied to related tasks, trained systems often
display unstable behavior.
More recently, researchers have begun developing new approaches to building
rich generative models that are capable of extracting useful, high-level
structured representations from high-dimensional input. The learned
representations have been shown to give promising results for solving a
multitude of novel learning tasks, even though these tasks may be unknown when
the generative model is being trained.
Although there has been recent progress, existing computational models are
still far from being able to represent, identify and learn the wide variety of
possible patterns and structure in real-world data. The goal of this workshop
is to catalyze the growing community of researchers working on learning rich
generative models, assess the current state of the field, discuss key
challenges, and identify future promising directions of investigation.
(More detailed background information is available at the workshop website,
http://www.mit.edu/~rsalakhu/workshop_nips2010/index.html)
Submission Instructions:
------------------------
We invite submission of extended abstracts to the workshop. Extended abstracts
should be 2-4 pages and adhere to the NIPS style
(http://nips.cc/PaperInformation/StyleFiles). Submissions should include the
title, authors' names, institutions and email addresses and should be sent in
PDF or PS file format by email to gentrans-nips2...@cs.toronto.edu
Submissions will be reviewed by the organizing committee on the basis of
relevance, significance, technical quality, and clarity. Selected submissions
may be accepted either as an oral presentation or as a poster presentation:
there will be a limited number of oral presentations.
We encourage submissions with a particular emphasis on:
1. Learning structured representations: How can machines extract invariant
representations from a large supply of high-dimensional highly-structured
unlabeled data? How can these representations be used to learn many different
concepts (e.g., visual object categories) and expand on them without disrupting
previously-learned concepts? How can these representations be used in multiple
applications?
2. Transfer Learning: How can previously-learned representations help learning
new tasks so that less labeled supervision is needed? How can this facilitate
knowledge representation for transfer learning tasks?
3. One-shot learning: Can we develop rich generative models that are capable of
efficiently leveraging background knowledge in order to learn novel categories
based on a single or a few training example? Are there models suitable for deep
transfer, or generalizing across domains, when presented with few examples?
4. Deep learning: Recently, there has been notable progress in learning deep
probabilistic generative models, including Deep Belief Networks, Deep Boltzmann
Machines, deep nonparametric Bayesian models, that contain many layers of
hidden variables. Can these models be extended to transfer learning tasks as
well as learning new concepts with only one or few examples? Can we use
representations learned by the deep models as an input to more structured
hierarchical Bayesian models?
5. Scalability and success in real-world applications: How well do existing
transfer learning models scale to large-scale problems including problems in
computer vision, natural language processing, and speech perception? How well
do these algorithms perform when applied to modeling high-dimensional
real-world distributions (e.g. the distribution of natural images)?
Organizers
----------
Ruslan Salakhutdinov, MIT
Ryan Adams, University of Toronto
Josh Tenenbaum, MIT
Zoubin Ghahramani, University of Cambridge
Tom Griffiths, University of California, Berkeley.
_______________________________________________
uai mailing list
uai@ENGR.ORST.EDU
https://secure.engr.oregonstate.edu/mailman/listinfo/uai
