May 2008 - Lisa_seminaires - mailman.iro.umontreal.ca

REMINDER: UdeM-McGill-MITACS machine learning seminar Tue May 27th, 10:00am, MC437
by Dumitru Erhan 26 May '08

26 May '08

This week's UdeM-McGill-MITACS seminar (see http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en ): Learning in the Sample Compression Framework by Mohak Shah, Probabilistic Vision Group Centre for Intelligent Machines McGill University Location: McConnell Engineering (McGill), room 437 Time: May 27th 2008, 10am This talk will focus on the algorithms’ learning capabilities under the Sample Compression framework highlighting some of the advantages over other learning paradigms. We will show how theoretically motivated algorithms with practical realizable generalization error bounds can be obtained and further how these risk bounds can be utilized to guide the model selection. As a special case, we will focus on decision based classifiers (e.g. learning conjunctions or disjunctions of data-dependent features) and show how performances comparable to and in some cases better than the state-of-the-art learning algorithms such as SVM can be obtained.

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UdeM-McGill-MITACS machine learning seminar Tue May 27th, 10:00am, MC437
by Dumitru Erhan 21 May '08

21 May '08

Next week's UdeM-McGill-MITACS seminar (see http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en) : Learning in the Sample Compression Framework by Mohak Shah, Probabilistic Vision Group Centre for Intelligent Machines McGill University Location: McConnell Engineering (McGill), room 437 Time: May 27th 2008, 10am This talk will focus on the algorithms’ learning capabilities under the Sample Compression framework highlighting some of the advantages over other learning paradigms. We will show how theoretically motivated algorithms with practical realizable generalization error bounds can be obtained and further how these risk bounds can be utilized to guide the model selection. As a special case, we will focus on decision based classifiers (e.g. learning conjunctions or disjunctions of data-dependent features) and show how performances comparable to and in some cases better than the state-of-the-art learning algorithms such as SVM can be obtained.

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[Fwd: RE: Montreal CIS Chapter Seminar: May 20, 2008 at 16:00 at ETS (Auditorium A-1600)]
by Yoshua Bengio 16 May '08

16 May '08

-------- Original Message -------- *From:* Cheriet, Mohamed *Sent:* May 1, 2008 3:32 PM *_______ ** **MONTREAL IEEE CIS MEETING / SEMINARS __________________*** * * *Date:** **Tuesday** Ma**y 20^th ,** 2008*** *Time:** 1**6**h* *Location:** **Auditorium A-1600, École de technologie supérieure*** *1100-Notre Dame W. (Corner Peel & Notre-Dame, Metro Bonaventure)* * *** *________________________________________________________________**____________________**_______* * * * * *Indexing and Searching Unconstrained Handwritten Documents* *Prof. **Venu Govindaraju* *University at Buffalo (SUNY Buffalo)*** *Dear Colleagues* *On behalf of the ExCom of Montreal IEEE CIS Chapter, I welcome you to attend the following exciting talk by a renowned researcher in the field. Please distribute the announcement among your students and colleagues. *** * * *Good News:** more exiting talks follow this year. I encourage you to attend, make suggestions, in order to make this Chapter a successful one. Thanks.* * * *I am looking forward to seeing you at the seminar.* *Best wishes,* *--- Prof. Mohamed Cheriet**, **Eng., Ph.D., SMIEEE* *Montreal IEEE CIS Chair* *ABSTRACT*** * * Recognition of handwritten documents has been a success in well-constrained domains, such as automatic recognition of handwritten postal addresses and validating bank check amounts. However, unconstrained offline handwriting recognition is still a very challenging task when lexicon size is very large. Thus, the large amount of handwritten material of the world, historical manuscripts and contemporary documents, are still inaccessible by search engines. We will describe our seminal work in real-time handwriting recognition which paved the way for the first deployment of end-to-end handwritten address interpretation system. We will also present loosely constrained applications such as indexing and searching hand-filled forms and unconstrained handwritten documents. We will conclude with an overview of our current research in using handwriting for web security. *Short BIO* Dr. Venu Govindaraju is a Professor of Computer Science and Engineering at the University at Buffalo (SUNY Buffalo). He received his B-Tech (Honors) from the Indian Institute of Technology (IIT), Kharagpur, India in 1986, and his Ph.D. from UB in 1992. Dr. Govindaraju is a Fellow of the IEEE (Institute of Electrical and Electronics Engineers) and a Fellow of the IAPR (International Association of Pattern Recognition). -- Yoshua Bengio

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REMINDER: UdeM-McGill-MITACS machine learning seminar Tues May 13th, 10:00am, PAA3195
by Hugo Larochelle 12 May '08

12 May '08

This 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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UdeM-McGill-MITACS machine learning seminar Wed May 13th, 10:00am, PAA3195
by Hugo Larochelle 08 May '08

08 May '08

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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