Lisa_seminaires Septembre 2009

lisa_seminaires@iro.umontreal.ca

2 participants
4 discussions

Fwd: Next Math-neuro presentation
by Yoshua Bengio 30 Sep '09

30 Sep '09

Pour ceux que les modèles motivés par la neuroscience intéresse, je connais Chris Eliasmith et il est bon: Begin forwarded message: > From: David THURA <david.thura(a)umontreal.ca> > Date: September 30, 2009 9:52:25 AM EDT (CA) > > Subject: Next Math-neuro presentation > > Dear Math-Neuro group, > > This message to remind you that a math-neuro session is > exceptionally planned tomorrow, *October the 1st (Thursday)*, 4-6 > PM, room 4106 of Pavillon Paul-G. Desmarais, Université de Montréal. > > Please, check out the map: > http://maps.google.com/maps/ms?client=safari&oe=UTF-8&ie=UTF8&hl=en&t=k&lci… > > Chris Eliasmith (University of Waterloo) will be giving an exciting > talk entitled "*How to build a brain: From single cells to > cognition.*" (see the abstract below) > > Please, join us for the stimulating discussion... > > Best regards, > > David > > Abstract: > > A central goal of theoretical neuroscience is to develop methods > that allow us to connect high-level hypotheses about brain function > to specific, detailed neural mechanisms that can be compared to > neural data. In this talk I will present the Neural Engineering > Framework (NEF), which we have developed to address this goal. I > will demonstrate how the framework relates to detailed physiological > data, and how it aids the construction of complex, large-scale > spiking neural models. I will discuss its application to > perceptual, motor, and cognitive modelling using specific examples. > Finally, I will provide a brief introduction to Nengo, the GUI > software environment we have developed that implements > the principles of the NEF. > > -- > David THURA, PhD > Department of physiology > University of Montreal > 2960 chemin de la tour > Montreal (Québec) H3T 1J4 CANADA > > Phone : 514-343-6111 x3302 > FAX : 514-343-2111 > E-mail : david.thura(a)umontreal.ca > Web : http://sites.google.com/site/thuradavidphd/ > Math-Neuro@UdM : http://sites.google.com/site/mathneuroatudm/

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Reminder: UdeM-McGill-MITACS machine learning seminar Fri, Sep 18th
by Guillaume Desjardins 17 Sep '09

17 Sep '09

Here's a friendly reminder for tomorrow's talk given by Dr. Massih-Reza Amini and entitled "Learning from Multiple Partially Observed Views" (see below for abstract). This talk will be held at the University of Montreal, Pavillon André-Aisenstadt (UdeM), room 3195 @ 11:00AM. See you there ! ---------- Forwarded message ---------- From: Guillaume Desjardins <guillaume.desjardins(a)gmail.com> Date: Fri, Sep 11, 2009 at 11:47 AM Subject: [Lisa_seminaires] RESCHEDULED: UdeM-McGill-MITACS machine learning seminar Fri, Sept To: lisa_seminaires(a)iro.umontreal.ca This is to advise you that the upcoming MITACS seminar has been rescheduled for Friday, September 18th from 11:00-12:00, due to some scheduling conflicts. I apologize for the short notice and hope many of you can make it. ---------- Forwarded message ---------- From: Guillaume Desjardins <guillaume.desjardins(a)gmail.com> Date: Tue, Sep 8, 2009 at 5:36 PM Subject: UdeM-McGill-MITACS machine learning seminar (NEW DATE & TIME) Mon, Sept 18th @11h00 To: lisa_seminaires(a)iro.umontreal.ca We start off the new academic year with a talk from Dr. Massih-Reza Amini entitled "Learning from Multiple Partially Observed Views". Speaker: Dr. Massih-Reza Amini Associate Professor of Pierre & Marie Curie University / Research Officer, NRC Institute for Information Technology Location: Pavillon André-Aisenstadt (UdeM), room 3195 Time: September 18th 2009 at 11h00 Title: Learning from Multiple Partially Observed Views Résumé: We address the problem of learning classifiers when observations have multiple views, some of which may not be observed for all examples. We assume the existence of view generating functions which may complete the missing views in an approximate way. This situation corresponds for example to learning text classifiers from multilingual collections where documents are not available in all languages. In that case, Machine Translation (MT) systems may be used to translate each document in the missing languages. We derive a generalization error bound for classifiers learned on examples with multiple artificially created views. Our result uncovers a trade-off between the size of the training set, the number of views, and the quality of the view generating functions. As a consequence, we identify situations where it is more interesting to use multiple views for learning instead of classical single view learning. An extension of this framework is a natural way to leverage unlabeled multi-view data in semi-supervised learning. Experimental results on a subset of the Reuters RCV1/RCV2 collections support our findings by showing that additional views obtained from MT may significantly improve the classification performance in the cases identified by our trade-off. _______________________________________________ Lisa_seminaires mailing list Lisa_seminaires(a)mercure.iro.umontreal.ca https://webmail.iro.umontreal.ca/mailman/listinfo/lisa_seminaires

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RESCHEDULED: UdeM-McGill-MITACS machine learning seminar Fri, Sept
by Guillaume Desjardins 11 Sep '09

11 Sep '09

This is to advise you that the upcoming MITACS seminar has been rescheduled for Friday, September 18th from 11:00-12:00, due to some scheduling conflicts. I apologize for the short notice and hope many of you can make it. ---------- Forwarded message ---------- From: Guillaume Desjardins <guillaume.desjardins(a)gmail.com> Date: Tue, Sep 8, 2009 at 5:36 PM Subject: UdeM-McGill-MITACS machine learning seminar (NEW DATE & TIME) Mon, Sept 18th @11h00 To: lisa_seminaires(a)iro.umontreal.ca We start off the new academic year with a talk from Dr. Massih-Reza Amini entitled "Learning from Multiple Partially Observed Views". Speaker: Dr. Massih-Reza Amini Associate Professor of Pierre & Marie Curie University / Research Officer, NRC Institute for Information Technology Location: Pavillon André-Aisenstadt (UdeM), room 3195 Time: September 18th 2009 at 11h00 Title: Learning from Multiple Partially Observed Views Résumé: We address the problem of learning classifiers when observations have multiple views, some of which may not be observed for all examples. We assume the existence of view generating functions which may complete the missing views in an approximate way. This situation corresponds for example to learning text classifiers from multilingual collections where documents are not available in all languages. In that case, Machine Translation (MT) systems may be used to translate each document in the missing languages. We derive a generalization error bound for classifiers learned on examples with multiple artificially created views. Our result uncovers a trade-off between the size of the training set, the number of views, and the quality of the view generating functions. As a consequence, we identify situations where it is more interesting to use multiple views for learning instead of classical single view learning. An extension of this framework is a natural way to leverage unlabeled multi-view data in semi-supervised learning. Experimental results on a subset of the Reuters RCV1/RCV2 collections support our findings by showing that additional views obtained from MT may significantly improve the classification performance in the cases identified by our trade-off.

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UdeM-McGill-MITACS machine learning seminar Mon, Sept 14th @15h00
by Guillaume Desjardins 08 Sep '09

08 Sep '09

We start off the new academic year with a talk from Dr. Massih-Reza Amini entitled "Learning from Multiple Partially Observed Views". Speaker: Dr. Massih-Reza Amini Associate Professor of Pierre & Marie Curie University / Research Officer, NRC Institute for Information Technology Location: Pavillon André-Aisenstadt (UdeM), room 3195 Time: September 14th 2009 at 15h00 Title: Learning from Multiple Partially Observed Views Résumé: We address the problem of learning classifiers when observations have multiple views, some of which may not be observed for all examples. We assume the existence of view generating functions which may complete the missing views in an approximate way. This situation corresponds for example to learning text classifiers from multilingual collections where documents are not available in all languages. In that case, Machine Translation (MT) systems may be used to translate each document in the missing languages. We derive a generalization error bound for classifiers learned on examples with multiple artificially created views. Our result uncovers a trade-off between the size of the training set, the number of views, and the quality of the view generating functions. As a consequence, we identify situations where it is more interesting to use multiple views for learning instead of classical single view learning. An extension of this framework is a natural way to leverage unlabeled multi-view data in semi-supervised learning. Experimental results on a subset of the Reuters RCV1/RCV2 collections support our findings by showing that additional views obtained from MT may significantly improve the classification performance in the cases identified by our trade-off.

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Lisa_seminaires Septembre 2009