Deep Learning and Unsupervised Feature Learning

In recent years, there has been a lot of interest in algorithms that learn feature hierarchies from unlabeled data. Deep learning methods such as deep belief networks, sparse coding-based methods, convolutional networks, 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.

Through invited talks, panels and discussions (see program schedule), we will attempt to address some of the more controversial topics in deep learning today, such as whether hierarchical systems are more powerful, the issues of scalability of deep learning, and what principles should guide the design of objective functions used to train these models.

The workshop will also invite paper submissions on the development of unsupervised feature learning and deep learning algorithms, theoretical foundations, inference and optimization, semi-supervised and transfer learning, and applications of deep learning and unsupervised feature learning to real-world tasks. Papers will be presented as oral or poster presentations (with a short spotlight presentation).

The workshop will also have two panel discussion sessions. The main topics of discussion will include:

* Whether/why hierarchical systems are really needed
* How to build hierarchical systems: advantages and disadvantages of bottom-up vs. top-down paradigm.
* Principles underlying learning of hierarchical systems: sparsity, reconstruction, (if supervised) what kind of supervision, how to learn invariances, etc.
* Issues of scalability of unsupervised feature learning and deep learning systems
* Major milestones and goals for the next 5 or 10 years
* Critiques of deep learning
* Real-world applications: what are challenging tasks and datasets?
* Relation to neuroscience: Can or should we design models that are more closely inspired by biological systems? Can we explain neural coding?

Panel discussions will be led by the members of the organizing committee as well as by prominent researchers from related fields.

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.


The proposed workshop builds on and extends the very successful Deep Learning and Unsupervised Feature Learning workshop held at NIPS 2010, which had over 150 attendees and received 30 research paper submissions.


The tentative timeline is (might be revised depending on the timing of notification of workshop acceptance):


August 30: Call for papers released
October 21: Paper submissions due
October 21 - November 7: Reviewing period
November 11: Notification of acceptance or rejection
December 1: Final version of papers due (for online proceedings)
December 16 or 17: Workshop*

* If possible, we'd prefer a Friday workshop date, which would allow us to organize a dinner for the attendees; but either day is fine.