Next week's seminar (see http://www.iro.umontreal.ca/article.php3? id_article=107&lang=en):

Deep belief networks are universal approximators and a recurrent neural network that learns to remember

by Ilya Sutskever, Department of Computer Science University of Toronto

Location: Pavillon André Aisenstadt (UdeM), room 3195 Time: May 13th 2008, 10:00am

The talk will consist of two parts. In the first part I will prove that narrow deep belief networks can approximate any distribution over binary vectors to arbitrary accuracy, and describe a simple greedy algorithm that can learn such networks in an impractical manner.

In the second part I will introduce a new kind of recurrent neural networks that can learn long term dependencies much better than standard recurrent neural networks.