Call for participation: Deep Learning and Representation Learning Workshop http://www.dlworkshop.org/

Friday, December 12, 2014 Palais des Congrès de Montréal/Convention and Exhibition Center, Montreal, Canada

Held in conjunction with Neural Information Processing Systems (NIPS) 2014.

Please see the tentative schedule here: http://www.dlworkshop.org/schedule

Scheduled location: Level 5; room 511 a,b, d,e

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 will present paper submissions in 2 poster sessions with several papers selected for oral presentation. Topics include:

• 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 and presentations by the participants, this workshop will showcase the latest advances in deep learning and address questions that are at the center of current deep learning research.

INVITED SPEAKERS:

Phil Blunsom (Oxford U.) Herbert Jaeger (Jacobs U. Bremen) Rich Schwartz (BBN) Vlad Mnih (Google DeepMind) Surya Ganguli (Stanford U.) Leon Bottou (Microsoft)

ORGANIZING COMMITTEE:

Yoshua Bengio Adam Coates Roland Memisevic Andrew Ng Daan Wierstra