Overview
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It is well understood that the performance of machine learning methods is heavily dependent on the choice of data representation (or features) on which they are applied. The rapidly developing field of representation learning is concerned with questions surrounding how we can best learn meaningful and useful representations of data. We take a broad view of the field, and include in it topics such as deep learning and feature learning, metric learning, kernel learning, compositional models, non-linear structured prediction, and issues regarding non-convex optimization.

Despite the importance of representation learning to machine learning and to application areas such as vision, speech, audio and NLP, there is currently no common venue for researchers who share a common interest in this topic. The goal of ICLR is to help fill this void.

A non-exhaustive list of relevant topics:
- unsupervised representation learning
- supervised representation learning
- metric learning and kernel learning
- dimensionality expansion, sparse modeling
- hierarchical models
- optimization for representation learning
- implementation issues, parallelization, software platforms, hardware
- applications in vision, audio, speech, and natural language processing, robotics and neuroscience.
- other applications

Submission Process
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ICLR2013 will use a novel publication model that will proceed as follows:

- Authors post their submissions on arXiv and send us a link to the paper. A separate, permanent website will be setup to handle the reviewing process, to publish the reviews and comments, and to maintain links to the papers.

- The ICLR program committee designates anonymous reviewers as usual.

- The submitted reviews are published without the name of the reviewer, but with an indication that they are the designated reviews. Anyone can write and publish comments on the paper (non anonymously). Anyone can ask the program chairs for permission to become an anonymous designated reviewer (open bidding). The program chairs have ultimate control over the publication of each anonymous review. Open commenters will have to use their real name, linked with their Google Scholar profile.

- Authors can post comments in response to reviews and comments. They can revise the paper as many time as they want, possibly citing some of the reviews.

- On March 15th 2013, the ICLR program committee will consider all submitted papers, comments, and reviews and will decide which papers are to be presented at the conference as oral or poster. Although papers can be modified after that date, there is no guarantee that the modifications will be taken into account by the committee.

- The best of the accepted papers (the top 25%-50%) will be given oral presentations at the conference. We have made arrangements for revised versions of selected papers from the conference to be published in a JMLR special topic issue.

- The other papers will be considered non-archival (like workshop presentations), and could be submitted elsewhere (modified or not), although the ICLR site will maintain the reviews, the comments, and the links to the arXiv versions.


Invited Speakers
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Jeff Bilmes (U. Washington)
Jason Eisner (JHU)
Geoffrey Hinton (U. Toronto)
Ruslan Salakhutdinov (U. Toronto)
Max Welling (U.Amsterdam)
Alan Yuille (UCLA)


General Chairs
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Yoshua Bengio, Université de Montreal
Yann LeCun, New York University

Program Chairs
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Aaron Courville, Université de Montreal
Rob Fergus, New York University
Chris Manning, Stanford University

Contact
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The organizers can be contacted at: iclr2013.programchairs@gmail.com