[Lisa_seminaires] UdeM-McGill-MITACS machine learning seminar Wed May 13th, 10:00am, PAA3195

Hugo Larochelle larocheh at iro.umontreal.ca
Jeu 8 Mai 16:55:23 EDT 2008


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.


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