Lisa_seminaires Mars 2009

lisa_seminaires@iro.umontreal.ca

2 participants
6 discussions

Kevin Murphy @ McGill on Friday
by Dumitru Erhan 26 Mar '09

26 Mar '09

Title: Computationally and statistically efficient methods for model selection in graphical models Speaker: Kevin Murphy (Stats/CS, U. British Columbia) Time/Place: Friday, 11am in Macdonald-Harrington G-01 Abstract: Graphical models are a way of representing conditional independence relationships between random variables using graphs. In this talk, we discuss ways of learning the structure of graphs from data. This is useful for visualizing relationships between variables in high-dimensional data, as well as for building density models for use in prediction, classification, clustering, etc. We will focus on undirected graphs (also called random fields), and in particular on methods based on L1-penalized maximum likelihood. First we extend existing results for the Gaussian and binary case to the more general case of conditional random fields and multi-state models. This requires that we replace the L1 penalty with a group L1 penalty, which poses various computational challenges. The second extension is to estimate the group structure (by clustering the variables) while simultaneously learning the graph structure. This technique relies on new bounds on the partition function for the positive definite matrix Laplace distribution, which also has applications in hierarchical Bayesian analysis of multiple related graphs.

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UdeM-McGill-MITACS machine learning seminar Fri March 27th, 15:00,
by Dumitru Erhan 26 Mar '09

26 Mar '09

Friday's seminar (see http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en): Machine Learning and Computational Linguistics: Tools of the Trade (or, The Data Acquisition Mental Bottleneck) by Jeremy Barnes Location: Pavillon André-Aisenstadt (UdeM), room 3195 Time: March 27th, 15h00 Computational Linguistics is a field where Machine Learning algorithms are ubiquitously applied. However, the gap between a good idea or a published paper and a commercial system is often large. The kinds of problems that can beset a machine learning practitioner in this field will be dissected and the tools and techniques used to overcome them explored. A particular emphasis is placed on the mapping of linguistics problems onto a machine learning problem and the complexities associated with obtaining and using linguistic datasets. In addition, common wisdom such as the "Data Acquisition Bottleneck" is sometimes cited both as a reason for the ineffectiveness of a machine learning solution and for abandoning promising approaches. This bottleneck will be analyzed and shown to be maybe more of a mental than a practical bottleneck. The seminar should be relevant to both computational linguists and machine learning practitioners, as well as anyone who is interested in the large-scale use of machine learning to solve problems in an industry environment. The tone will be relatively informal. Bio: Jeremy Barnes recently finished a 8 1/2 year stint at Idilia Inc, a Montreal-based company that has developed and commercialized Word Sense Disambiguation, knowledge extraction and paraphrasing technology see (www.idilia.com). Beginning as the first R&D employee in the company, he artitected the technology around machine learning and has applied machine learning to numerous unsolved or semi-solved problems in the domain.

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Seminar at McGill on March 26 at 11:30am
by Dumitru Erhan 25 Mar '09

25 Mar '09

Note that the seminar will be held in McConnell Engineering Building, room 103 ------- Sandra Zilles (postdoc at UofA) will be visiting again March 26-27, and will be giving a talk on Thu March 26, 11:30am. Title: Cooperative teaching, active learning, and sample compression Abstract: The problem of how a teacher and a learner can cooperate in the process of learning concepts from examples in order to minimize the required sample size without "coding tricks" has been widely addressed, yet without achieving teaching and learning protocols that meet what intuitively seems an optimal choice for selecting samples in teaching. In this presentation, two models of cooperative teaching and learning are introduced. The model of "subset teaching sets" is based on the idea that both teacher and learner can iteratively exploit the assumption that the partner is cooperative, comparable to a two-player game. The corresponding variant of the teaching dimension turns out to be nonmonotonic with respect to subclasses of concept classes. That means, there are concept classes that become easier to teach when they are expanded. We will discuss why this nonmonotonicity might be natural in cooperative teaching scenarios. A second model introduced overcomes the nonmonotonicity of the subset teaching dimension. "Recursive teaching sets" are based on nested concept classes. The nesting here reflects the complexity of teaching subclasses of the given concept class. We will see how both new models can drastically reduce the sample size required for teaching a concept - without using coding tricks (for a simple and intuitive notion of "coding trick"). For instance, monomials can be taught with only two examples independent of the number of variables (in both models). It will be shown how this theory of cooperative learning can open new ways of (a) showing inherent connections between teaching and active learning and (b) tackling a long-standing open question on sample compression. (joint work with Robert Holte, Steffen Lange, and Martin Zinkevich) Hope to see you there! Doina

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Soutenance de thèse de Hugo Larochelle
by Pascal Vincent 13 Mar '09

13 Mar '09

Vous êtes tous cordialement invités à assister à la soutenance de thèse de doctorat de Hugo Larochelle, qui aura lieu ce vendredi 13 mars à 14h00 au local 3195 du pavillon André-Aisenstadt. TITRE: Étude de techniques d’apprentissage non-supervisé pour l’amélioration de l’entraînement supervisé de modèle connexionnistes RÉSUMÉ: Popularisé dans les années 80, le réseau de neurones artificiel est un modèle puissant d'apprentissage automatique. Grâce à la découverte d'une représentation riche de son entrée, ce type de modèle est capable de résoudre des tâches complexes liées entre autre à la vision et au traitement de la langue. Malheureusement, l'entraînement d'un réseau de neurones est un problème difficile, à un point tel que les réseaux typiquement entraînés contiennent souvent relativement peu de neurones cachés, organisés en une seule couche. Je présenterai donc les travaux de ma thèse de doctorat, qui visent à tirer profit de l'apprentissage non-supervisé afin d'améliorer la performance de différents types de réseaux de neurones supervisés. L'utilisation de l'apprentissage non-supervisé permet ici d'augmenter significativement la taille des réseaux pouvant être entraînés avec succès. En premier lieu, j'illustrerai les bénéfices de la combinaison des apprentissages non-supervisé et supervisé dans le cadre de l'entraînement d'une machine de Boltzmann restreinte, un type particulier de réseaux de neurones à une seule couche cachée. Ensuite, je présenterai une approche similaire basée sur un réseau de neurone autoencodeur et permettant d'entraîner un réseau de neurones profond, c'est-à-dire à plusieurs couches cachées. L'avantage de l'utilisation d'un réseau de neurones profond sera démontrée sur une série de problèmes de classification d'images générées à partir de plusieurs facteurs de variation. Ensuite, je montrerai qu'il est possible d'obtenir une amélioration additionnelle des performances en encourageant explicitement un réseau de neurones à extraire une représentation de l'entrée qui soit robuste, c'est-à-dire qui soit invariante à l'absence d'une partie des éléments d'entrée. Finalement, je décrirai une méthode simple pour introduire des interactions d'inhibition et d'excitation entre les neurones cachés, permettant d'extraire une représentation plus riche de l'entrée et pouvant aussi améliorer la performance d'un réseau profond. COMPOSITION DU JURY: Président-Rapporteur: Pascal Vincent Directeur de Recherche: Yoshua Bengio Examinateur Externe: Geoffrey Hinton Membre du Jury: Douglas Eck Représentante du Doyen: Nathalie Loye

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Reminder: UdeM-McGill-MITACS machine learning seminar Fri March 13th,
by Dumitru Erhan 12 Mar '09

12 Mar '09

Tomorrow's seminar (see http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en): Recent developments in learning deep networks by Geoffrey Hinton University of Toronto and Canadian Institute for Advanced Research Location: Pavillon André-Aisenstadt (UdeM), room 3195 Time: March 13th 2009, 10h30 It is possible to learn deep belief nets that are good at object recognition by composing a number of simple modules, each of which contains only one layer of hidden units. The layers are learned one at a time by treating the hidden activities of one module as the data for training the next module. I will start by describing a new method for learning each module that is faster than previous methods and gives better performance on test data. Then I will describe a more powerful basic module for deep learning. The module allows third-order, multiplicative interactions in which hidden units gate the pairwise interactions between visible units. A technique for factoring the third-order interactions leads to a learning module that has a simple learning rule based on pairwise correlations. This module looks remarkably like modules that have been proposed by both biologists trying to explain the responses of neurons and engineers trying to create systems that can recognize objects. The talk will describe joint work with Tijmen Tieleman and Roland Memisevic.

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UdeM-McGill-MITACS machine learning seminar Fri March 13th, 10:30,
by Dumitru Erhan 10 Mar '09

10 Mar '09

This week's seminar (see http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en): Recent developments in learning deep networks by Geoffrey Hinton University of Toronto and Canadian Institute for Advanced Research Location: Pavillon André-Aisenstadt (UdeM), room 3195 Time: March 13th 2009, 10h30 It is possible to learn deep belief nets that are good at object recognition by composing a number of simple modules, each of which contains only one layer of hidden units. The layers are learned one at a time by treating the hidden activities of one module as the data for training the next module. I will start by describing a new method for learning each module that is faster than previous methods and gives better performance on test data. Then I will describe a more powerful basic module for deep learning. The module allows third-order, multiplicative interactions in which hidden units gate the pairwise interactions between visible units. A technique for factoring the third-order interactions leads to a learning module that has a simple learning rule based on pairwise correlations. This module looks remarkably like modules that have been proposed by both biologists trying to explain the responses of neurons and engineers trying to create systems that can recognize objects. The talk will describe joint work with Tijmen Tieleman and Roland Memisevic.

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Lisa_seminaires Mars 2009