Lisa_seminaires Novembre 2007

lisa_seminaires@iro.umontreal.ca

5 participants
8 discussions

NEW LOCATION: UdeM-McGill-MITACS machine learning seminar, PAA 1360, Fri Nov 30, 11:30am
by Hugo Larochelle 29 Nov '07

29 Nov '07

This week's seminar (see http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en): A Bayesian approach to analyze water tank signals in the Pierre Auger experiment by Balazs Kegl, Laboratoire de l’Accélérateur Linéaire Université Paris-Sud 11 Location: Pavillon André-Aisenstadt (UdeM), room 1360 Time: November 30th 2007, 11h30 The objective of the Pierre Auger experiment (auger.org) is to study the properties of ultra-high energy cosmic ray particles. When one if these particles collides with the atmosphere, it generates a huge shower of atmospheric particles that covers several square kilometers on the Earth’s surface. The surface detector of the Auger Observatory (built on the pampas of Argentina) consists of 1600 water tanks spaced at 1.5 km on a regular hexagonal grid that detect atmospheric shower particles through their interaction with water. To analyze the water tank signals, we have built an elaborate generative model based on physical laws and simulations of both the showers and the detector. To estimate the model parameters, we use a Bayesian approach based on reversible jump Monte Carlo Markov chains. This technique allows us to estimate the number of muonic mixture components in the signal which is one of the best indicators of the nature of the original cosmic ray particle. In the first part of the talk we introduce the basic physics behind the experiment and describe the statistical model. In the second part we show the Bayesian technique that we are using to estimate the parameters and present some preliminary results. _______________________________________________ Lisa_seminaires mailing list Lisa_seminaires(a)mercure.iro.umontreal.ca https://webmail.iro.umontreal.ca/mailman/listinfo/lisa_seminaires

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UdeM-McGill-MITACS machine learning seminar, PAA 6214, Fri Nov 30, 11:30am
by Hugo Larochelle 26 Nov '07

26 Nov '07

This week's seminar (see http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en): A Bayesian approach to analyze water tank signals in the Pierre Auger experiment by Balazs Kegl, Laboratoire de l’Accélérateur Linéaire Université Paris-Sud 11 Location: Pavillon André-Aisenstadt (UdeM), room 6214 Time: November 30th 2007, 11h30 The objective of the Pierre Auger experiment (auger.org) is to study the properties of ultra-high energy cosmic ray particles. When one if these particles collides with the atmosphere, it generates a huge shower of atmospheric particles that covers several square kilometers on the Earth’s surface. The surface detector of the Auger Observatory (built on the pampas of Argentina) consists of 1600 water tanks spaced at 1.5 km on a regular hexagonal grid that detect atmospheric shower particles through their interaction with water. To analyze the water tank signals, we have built an elaborate generative model based on physical laws and simulations of both the showers and the detector. To estimate the model parameters, we use a Bayesian approach based on reversible jump Monte Carlo Markov chains. This technique allows us to estimate the number of muonic mixture components in the signal which is one of the best indicators of the nature of the original cosmic ray particle. In the first part of the talk we introduce the basic physics behind the experiment and describe the statistical model. In the second part we show the Bayesian technique that we are using to estimate the parameters and present some preliminary results.

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Visite et séminaire de Renato De Mori le 5 décembre à McGill
by Yoshua Bengio 26 Nov '07

26 Nov '07

Bonjour, Pour ceux qui s'intéressent aux systèmes de dialogue et à la compréhension du langage parlé, ou voudraient revoir Renato De Mori, il sera de passage à Montréal la première semaine de décembre: Begin forwarded message:

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2 interesting talks this week
by Hugo Larochelle 20 Nov '07

20 Nov '07

There isn't a MITACS seminar this week, however the following talks might be of interest to you. Here is the first one at UdeM (see below for the second one at McGill): BoostCluster: Boosting Clustering by Pairwise Constraints by Rong Jin, Michigan State University Location: Pavillon Andre-Aisenstadt (UdeM), room 1177 Time: November 23rd 2007, 14h00 Data clustering is an important task in many disciplines. A large number of studies have attempted to improve clustering by using the side information that is often encoded as pairwise constraints. However, these studies focus on designing special clustering algorithms that can effectively exploit the pairwise constraints. We present a boosting framework for data clustering, termed as BoostCluster, that is able to iteratively improve the accuracy of any given clustering algorithm by exploiting the pairwise constraints. The key challenge in designing a boosting framework for data clustering is how to influence an arbitrary clustering algorithm with the side information since clustering algorithms by definition are unsupervised. The proposed framework addresses this problem by dynamically generating new data representations at each iteration that are, on the one hand, adapted to the clustering results at previous iterations by the given algorithm, and on the other hand consistent with the given side information. Our empirical study shows that the proposed boosting framework is effective in improving the performance of a number of popular clustering algorithms (K-means, partitional SingleLink, spectral clustering), and its performance is comparable to the state-of-the-art algorithms for data clustering with side information. Also, Rong Jin is giving another talk at McGill on Wednesday 21st, 11h30, McConnell Building (McGill), room 103: Title: Batch Mode Active Learning Abstract: The goal of active learning is to select the most informative examples for manual labeling. Most of the previous studies in active learning have focused on selecting a {\em single} unlabeled example in each iteration. This could be inefficient since the classification model has to be retrained for every labeled example. In this paper, we present a framework for ``{\bf batch mode active learning}'' that applies the Fisher information matrix to select a number of informative examples simultaneously. The key computational challenge is how to efficiently identify the subset of unlabeled examples that can result in the largest reduction in the Fisher information. To resolve this challenge, we propose efficient algorithms that are based on bound optimization and the property of submodular functions. Our empirical studies with text categorization and medical image classification show that the proposed batch mode active learning algorithm is more effective than the state-of-the-art algorithms for active learning.

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REMINDER: UdeM-McGill-MITACS machine learning seminar Fri Nov. 16, 11:30am, MC 437
by Hugo Larochelle 15 Nov '07

15 Nov '07

This week's seminar (see http://www.iro.umontreal.ca/article.php3? id_article=107&lang=en): Bayesian Reinforcement Learning by Mohammad Ghavamzadeh, Department of Computing Science University of Alberta Location: McConnell Building (McGill), room 437 Time: November 16th 2007, 11h30 Reinforcement learning is a class of learning problems in which an agent interacts with an unfamiliar, dynamic and stochastic environment, and whose goal is to optimize some measure of its long- term performance. Despite extensive research and numerous successes in a number of different domains, there remain several fundamental obstacles hindering the widespread application of reinforcement learning methodology to real-world problems. Recent advances have shown that Bayesian approach to reinforcement learning offers viable solutions to some of these major limitations, such as the lack of confidence intervals for performance predictions, the difficulty of appropriately reconciling exploration with exploitation, and the lack of a systematic method for encoding prior knowledge and for formulating domain assumptions. Policy gradient methods are reinforcement learning algorithms that adapt a parameterized policy by following a performance gradient estimate. This talk will present two Bayesian policy gradient algorithms. These algorithms use Gaussian processes to define prior distribution over the performance gradient, and obtain closed-form expressions for its posterior distribution, conditioned on the observed data. The posterior mean serves as the policy gradient estimate and is used to update the policy, while the posterior covariance allows us to gauge the reliability of the update. In the first algorithm, the basic observable unit, upon which learning and inference are based, is a complete trajectory, allowing the algorithm to handle non-Markovian systems. The second algorithm takes advantage of the Markov property of the system trajectories and uses individual state-action-reward transitions as its basic observable unit. This helps reduce variance in the gradient estimates and facilitates handling continuing problems. _______________________________________________ Lisa_seminaires mailing list Lisa_seminaires(a)mercure.iro.umontreal.ca https://webmail.iro.umontreal.ca/mailman/listinfo/lisa_seminaires

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UdeM-McGill-MITACS machine learning seminar Fri Nov. 16, 11:30am, MC 437
by Hugo Larochelle 12 Nov '07

12 Nov '07

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Fwd: COLLOQUE CRM-ISM-GERAD DE STATISTIQUE: C. Crainiceanu (Le vendredi 16 novembre 2007)
by Yoshua Bengio 05 Nov '07

05 Nov '07

Pour ceux qui ne veulent pas m'entendre radoter à McGill le 16 novembre il y a ce talk à Concordia qui pourrait être bien: Begin forwarded message: > From: CRM <activites(a)CRM.UMontreal.CA> > Date: November 5, 2007 8:29:05 AM EST (CA) > To: lofficiel <lofficiel(a)CRM.UMontreal.CA> > Subject: COLLOQUE CRM-ISM-GERAD DE STATISTIQUE: C. Crainiceanu (Le > vendredi 16 novembre 2007) > > ****************************************************************** > COLLOQUE CRM-ISM-GERAD DE STATISTIQUE > ****************************************************************** > > CONFERENCIER(S) / SPEAKER(S) : > Ciprian Crainiceanu > (Johns Hopkins University) > > > TITRE / TITLE : > "Multilevel Functional Principal Component Analysis" > > > LIEU / PLACE : > Concordia, Library Building, 1400 Blvd. de Maisonneuve O. Salle / > Room LB-921-04 > > DATE : > Le vendredi 16 novembre 2007 / Friday, November 16, 2007 > > HEURE / TIME : > 15 h 30 / 3:30 p.m. > > RESUME / ABSTRACT : > Modern research data have become increasingly complex, raising non- > traditional modeling and inferential challenges. In particular, > advancements in technology and computation have made recording and > processing of functional data possible. Examples of functional data > are time series of electroencephalographic (EEG) activity, > anatomical shape, and functional MRI. The purpose of this talk is > to describe statistical models for feature extraction from single- > level (one or multiple functions per subject at one visit) and > clustered or longitudinal (one or multiple functions per subject at > multiple visits) functional data having a large number of subjects > and large within- and between-subject heterogeneity. We introduce > the framework and inferential tools for multilevel functional data > (MFD) obtained by recording of functional characteristics at > multiple visits. Though motivated by a novel experimental setting, > the proposed methodology is general, with potential broad > applicability to many high-throughput scientific studies. A > prototypical example of MFD is the Sleep Heart Health Study (SHHS), > which contains electroencephalographic (EEG) signals for each > subject at two visits. > > ****************************************************************** > > http://www.crm.umontreal.ca/cgi/Stats.csh

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séminaire de Yoshua Bengio dans 2 semaines à McGill
by Yoshua Bengio 02 Nov '07

02 Nov '07

Bonjour, Je donne un séminaire pour une audience non-spécialisée (informatique et génie électrique) dans deux semaines à McGill, sur la question de l'intelligence artificielle via l'apprentissage machine et des problèmes difficiles d'optimisation qui sont impliqués là-dedans. L'annonce est là: http://www.cse.mcgill.ca/Fall07/YB.pdf Friday Nov 16 at 14:30 in McConnell Engineering Bldg Room 103 Learning Deep Architectures for AI Theoretical results in complexity theory of circuits and in non-parametric statistics 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 appears to be a fundamentally difficult optimization task, so approximate numerical optimization schemes are called for. Learning algorithms such as those for Deep Belief Networks have recently been proposed which make a dent in this difficult optimization task, beating the state-of-the-art in certain areas. This talk discusses the motivations and principles regarding learning algorithms for deep architectures and in particular for those based on unsupervised learning such as Deep Belief Networks, using as building blocks single-layer models such as Restricted Boltzmann Machines. -- Yoshua Bengio

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Lisa_seminaires Novembre 2007