Neuro-Thursday
Thusday, December 6 - 8:30 AM - Noon
This year, NIPS is inaugurating "Neuro-Thursday," designed to let Neuroscience researchers coming for the Workshops also experience part of the main NIPS program. On Thursday, December 6 (the final day of the Conference), the NIPS program will be devoted to Neuroscience, and will consist of a fascinating invited talk by Professor Manabu Tanifuji (Riken) on the monkey visual cortex, plus six outstanding plenary talks. The program will end at the usual time, allowing the attendees not attending the afternoon Workshop (below) to catch the bus to the Whistler Workshops. In addition, all of the Neuroscience posters will take place on Wednesday night, allowing early arrivals to view yet more exciting work. The Wednesday night poster program will also contain many posters on Machine learning and Computer Vision, focused on topics that are also relevant to Neuroscience.
All of the Thursday morning events (including the Wednesday night Poster Session and the 5:20PM Spotlight session) will be available for the low "Neuro-Thursday" registration rate of $50.
For those attending the entire Conference, "Neuro-Thursday" is included in the registration price.
Deep Learning Satellite Meeting: Foundations and Future Directions
Thursday, December 6 - 2:00 to 5:30 PM
Theoretical results strongly suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need "deep architectures", which are composed of multiple levels of non-linear operations (such as in neural nets with many hidden layers). Searching the parameter space of deep architectures is a difficult optimization task, but learning algorithms (e.g. Deep Belief Networks) have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas.
This meeting is intended to bring together researchers interested in the question of deep learning in order to review the current algorithms' principles and successes, but also to identify the challenges, and to formulate promising directions of investigation. Besides the algorithms themselves, there are many fundamental questions that need to be addressed: What would be a good formalization of deep learning? What new ideas could be exploited to make further inroads to that difficult optimization problem? What makes a good high-level representation or abstraction? What type of problem is deep learning appropriate for?
There is no charge for this meeting or for the bus to Whistler that will leave after the meeting, however, a separate registration is required.
To register: