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Idiap Research Institute
Centre du Parc
Rue Marconi 19
PO Box 592
CH - 1920 Martigny
Switzerland

T +41 27 721 77 11
F +41 27 721 77 12

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Idiap Research Institute

Research & Applications
The Idiap Research Institute will participate in the robotics festival organized this Saturday at EPFL! Apr 18, 2013
"Festival Robotique" Saturday April 20, 2013, EPFL Lausanne.
Best paper award for Alexandre Heili and Jean-Marc Odobez Jan 30, 2013
at the Workshop on Performance Evaluation of Tracking and Surveillance (PETS), 2013.
Emogen, winners of the IMD Startup Competition 2013 Dec 21, 2012
The venture EmoGen, participant of the ICC’2012 and new Idiap spin-off, has been selected amongst the winners of the IMD Startup Competition 2013.
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TECHNOLOGY TRANSFER
International Create Challenge - cradle of innovation in Valais Apr 04, 2013
"L'actualité économique décryptée avec Pascal Gentinetta, directeur d'economiesuisse"
EmoGen reçoit la Bourse The Ark et passe la première phase du concours Venture Kick Mar 08, 2013
Crédit: Cédric Luisier, TheArk, 08.03.2013 Source: www.theark.ch.
Koemei, Idiap spin-off, starts collaboration with Aljazeera Feb 19, 2013
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EDUCATION & JOBS
Gelareh Mohammadi is selected as one of the recipients of the 2013 Google Anita Borg Memorial Scholarship May 15, 2013
Idiap invites applications for a permanent Researcher or Senior Researcher position May 02, 2013
The main focus of this search is for highly qualified candidates, with evidence of strong research, PhD student supervision, and project management capabilities.
On April 26, 2013, Tatiana Tommasi successfully defended her PhD thesis entitled "Learning to Learn by Exploiting Prior Knowledge" Apr 29, 2013
The work described in her thesis takes place in the context of visual recognition and category detection, in all scenarios where there is a need for learning a new category model but it is not possible or expensive, to obtain large amounts of annotated training data. The main contribution of the thesis of Tatiana Tommasi is to cast the problem of learning to learn from small samples into the max-margin classifiers framework, providing to the community principled algorithms for open ended learning of object categories from few samples. The power and generality of the results achieved in the thesis has further been demonstrated in other domains, such as control of prosthetic hands for amputees.
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