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Platform to Share Feature Extraction Methods
François Fleuret

Wed Dec 08 07:30 PM -- 11:59 PM (PST) @ Georgia A
Event URL: http://www.mash-project.eu/ »

The MASH project is a three year European initiative which started early in 2010. Its main objective is to develop new tools and methods to help the collaborative design of very large families of features extractors for machine learning. This is investigated through the development of an open web platform which allows to submit implementations of feature extractors, browse extractors already contributed to the system, launch experiments, and analyze previous experimental results to focus the design on the identified weakness of the system. Performance of the designed learning architectures are evaluated on image recognition, object detection, and goal planning. The former targeting both a video-game-like 3d simulator and a real robot controlled remotely. We want mainly to demonstrate the features of the platform to the NIPS attendees, and show both the development tools, and the applications we have already implemented.

Author Information

François Fleuret (University of Geneva)

François Fleuret got a PhD in Mathematics from INRIA and the University of Paris VI in 2000, and an Habilitation degree in Mathematics from the University of Paris XIII in 2006. He is Full Professor in the department of Computer Science at the University of Geneva, and Adjunct Professor in the School of Engineering of the École Polytechnique Fédérale de Lausanne. He has published more than 80 papers in peer-reviewed international conferences and journals. He is Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence, serves as Area Chair for NeurIPS, AAAI, and ICCV, and in the program committee of many top-tier international conferences in machine learning and computer vision. He was or is expert for multiple funding agencies. He is the inventor of several patents in the field of machine learning, and co-founder of Neural Concept SA, a company specializing in the development and commercialization of deep learning solutions for engineering design. His main research interest is machine learning, with a particular focus on computational aspects and sample efficiency.

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