The Deep Learning and Representation Learning Workshop will be held in conjunction with Neural Information Processing Systems (NIPS) on Friday, December 12, 2014, in Montreal, Canada.
Deep Learning algorithms attempt to discover good representations, at multiple levels of abstraction. There has been rapid progress in this area in recent years, both in terms of algorithms and in terms of applications, but many challenges remain. The workshop aims at bringing together researchers in that field and discussing these challenges, brainstorming about new solutions.
The workshop invites paper submissions that will be either presented as oral or in poster format. We encourage submissions on the following (non-exhaustive) list of topics:
* deep learning algorithms and models (supervised or unsupervised, including about building blocks of deep nets, like RBMs and auto-encoders, etc.)
* inference and optimization algorithms
* semi-supervised, transfer learning, and multi-modal algorithms
* theoretical foundations of deep learning (both supervised and unsupervised)
* applications of deep learning (convolutional networks, word and sentence representation models, etc.)
Through invited talks, a panel discussion and presentations by the participants, this workshop will showcase the latest advances in deep learning and address questions that are at the centre of current deep learning research. Panel discussions will be led by the members of the organizing committee as well as by prominent representatives of the machine learning, computer vision and natural language processing communities.
Important dates (tentative):