Lisa_seminaires Janvier 2009

lisa_seminaires@iro.umontreal.ca

1 participants
3 discussions

Reminder: UdeM-McGill-MITACS machine learning seminar Fri January
by Dumitru Erhan 29 Jan '09

29 Jan '09

Tomorrow's seminar (see http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en): New architectures and algorithms for solving deep-memory POMDPs by Daan Wierstra, IDSIA, Switzerland Location: Pavillon André-Aisenstadt (UdeM), room 3195 Time: January 30th 2009, 15h00 Reinforcement learning in partially observable environments is a difficult problem, and appears to be intractable for classical reinforcement learning methods. We present two new approaches for dealing with partial observability. One pertains to searching the space of memory cell architectures in order to better capture long-term time dependencies. The second approach involves the use of natural gradients in stochastic search as an alternative to conventional evolutionary methods.

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Fwd: next math-neuro: 27th Jan (Tuesday)
by Dumitru Erhan 26 Jan '09

26 Jan '09

Begin forwarded message: > From: Sergiy Yakovenko <sergiy.yakovenko(a)gmail.com> > Date: January 22, 2009 9:02:20 PM EST (CA) > To: Undisclosed-recipients: <>; > Cc: Timothy Lillicrap <tim(a)biomed.queensu.ca> > Subject: next math-neuro: 27th Jan (Tuesday) > > Dear Math-Neuro group, > > Our next Math-Neuro meeting will be next week on 27th of January (Tuesday), 4-6 PM, room 4106 of Pav. P.-G. Desmarais. Timothy Lillicrap of Queen's University will present an exciting talk entitled "How do the properties of the musculoskeletal system shape the activity of motor cortex neurons?" > > Regards, > Sergiy & Paul > > Sergiy Yakovenko > Département de physiologie > Université de Montréal > Tel: (514) 343-6111 ext 3333 > > DIRECTIONS: > http://maps.google.com/maps/ms?client=safari&oe=UTF-8&ie=UTF8&hl=en&t=k&lci… > > > >

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UdeM-McGill-MITACS machine learning seminar Fri January 30th, 15:00,
by Dumitru Erhan 26 Jan '09

26 Jan '09

This week's seminar (see http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en): New architectures and algorithms for solving deep-memory POMDPs by Daan Wierstra, IDSIA, Switzerland Location: Pavillon André-Aisenstadt (UdeM), room 3195 Time: January 30th 2009, 15h00 Reinforcement learning in partially observable environments is a difficult problem, and appears to be intractable for classical reinforcement learning methods. We present two new approaches for dealing with partial observability. One pertains to searching the space of memory cell architectures in order to better capture long-term time dependencies. The second approach involves the use of natural gradients in stochastic search as an alternative to conventional evolutionary methods.

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Lisa_seminaires Janvier 2009