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ICLR 2015 : International Conference on Learning Representations 2015


When May 7, 2015 - May 9, 2015
Where San Diego, CA
Submission Deadline Dec 19, 2014
Categories    machine learning   data representation

Call For Papers

ICLR is an annual conference sponsored by the Computational and Biological Learning Society

ICLR 2015 will be held May 7 - 9, 2015 in San Diego, CA.

We will be co-located with AISTATS 2015, with May 9 being a joint ICLR/AISTATS day.


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 was no venue for researchers who share a common interest in this topic. The goal of ICLR has been to help fill this void.

The conference follows a recently introduced open reviewing and open publishing publication process, which is explained in further detail here.

Yoshua Bengio & Yann Lecun, General Chairs


An Open Reviewing Paradigm

ICLR uses an open publication and reviewing model.

- Submissions to ICLR are posted on arXiv prior to being submitted to the conference.
- Authors submit their paper to either the ICLR conference track or workshop track via the the openreview website.
- After the authors have submitted their papers via, 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 openly (non-anonymously) write and publish comments on the paper. 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 names, linked with their Google Scholar profiles.
- Authors can post comments in response to reviews and comments. They can revise the paper as many times as they want, possibly citing some of the reviews.
- The ICLR program committee will consider all submitted papers, comments, and reviews and will decide which papers are to be presented in the conference track, which are to be presented in the workshop track, and which will not appear at ICLR.
- Papers that are presented in the workshop track or are not accepted will be considered non-archival, and may be submitted elsewhere (modified or not), although the ICLR site will maintain the reviews, the comments, and the links to the arXiv versions.


Important Dates
19 Dec. 2014 Authors submit papers to ICLR 2015 via (A link will be provided)
26 Dec. 2014 Authors update their submissions with the arXiv number and URL if they were not available on 19 Dec. 2014.
02 Jan. 2015 Reviewers receive their assignments.
30 Jan. 2015 Reviewers submit their reviews.
06 Feb. 2015 Authors post their initial responses to the reviews.
06 Mar. 2015 End of discussion period for papers.
20 Mar. 2015 Decisions sent to authors.
06 Apr. 2015 Deadline to register for the hotel at the conference rate.

Related Resources

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ICPR 2018   24th International Conference on Pattern Recognition
ICONIP 2017   International Conference on Neural Information Processing
ICAISC 2018   International Conference on Artificial Intelligence and Soft Computing
ICMLA 2017   16th IEEE International Conference On Machine Learning And Applications
SENSE 2017   The first workshop on Sense, Concept and Entity Representations and their Applications, co-located with EACL 2017
LOPAL 2018   International Conference on Learning and Optimization Algorithms: Theory and Applications