Call for Papers: Deep Learning and Unsupervised Feature Learning Workshop
held in conjunction with Neural Information Processing Systems (NIPS 2011)
December 16 or 17, 2011, Granada, Spain
(This is a one-day workshop, and the date will be determined soon.)
In recent years, there has been a lot of interest in algorithms that learn feature representations from unlabeled data. Deep learning algorithms such as deep belief networks, sparse coding-based methods, convolutional networks, ICA methods, and deep Boltzmann machines have shown promise and have already been successfully applied to a variety of tasks in computer vision, audio processing, natural language processing, information retrieval, and robotics. In this workshop, we will bring together researchers who are interested in deep learning and unsupervised feature learning, review the recent technical progress, discuss the challenges, and identify promising future research directions.
The workshop invites paper submissions that will be either presented as oral or in poster format. Through invited talks, panel discussions and presentations by the participants, this workshop attempts to address some of the more controversial topics in deep learning today, such as whether hierarchical systems are more powerful, and what principles should guide the design of objective functions used to train these models. Panel discussions will be led by the members of the organizing committee as well as by prominent representatives of the vision and neuroscience communities.
The goal of this workshop is two-fold. First, we want to identify the next big challenges and propose research directions for the deep learning community. Second, we want to bridge the gap between researchers working on different (but related) fields, to leverage their expertise, and to encourage the exchange of ideas with all the other members of the NIPS community.
– Submission deadline: October 21, 2011 (Updated)
– Acceptance notification: November 11, 2011
– Workshop date: December 16 or 17, 2011 (This is a one-day workshop, and the date will be determined soon.)
A tentative schedule is available at:
We solicit submissions of unpublished research papers. Papers should be at most 8 pages (plus 1 additional page containing references only) and must satisfy the formatting instructions of the NIPS 2011 call for papers.
Style files are available at http://nips.cc/PaperInformation/StyleFiles.
Please note that the reviewing is double blind, so your manuscript should not contain authors’ identifying information.
Papers should be submitted through https://cmt.research.microsoft.com/DLUFL2011/ no later than 23:59 EST on Friday, October 21, 2011.
We encourage submissions on the following and related topics:
* unsupervised feature learning algorithms
* deep learning algorithms
* semi-supervised and transfer learning algorithms
* inference and optimization
* theoretical foundations of unsupervised learning
* theoretical foundations of deep learning
* applications of deep learning and unsupervised feature learning
The best papers will be awarded by an oral presentation, all other papers will have a poster presentation accompanied by a short spotlight presentation.
* Adam Coates – Stanford University
* Yoshua Bengio – University of Montreal
* Yann LeCun – New York University
* Nicolas Le Roux – INRIA
* Andrew Y. Ng – Stanford University