FRANÇOIS FLEURET

head of the Machine Learning group
Machine learning
IDIAP Research Institute
Switzerland

Professor Engineering
Biography

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 the head of the Machine Learning group at the Idiap Research Institute, Switzerland, since 2007, and adjunct faculty at the École Polytechnique Fédérale de Lausanne (EPFL) since 2011, where he teaches machine learning. 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 (TPAMI) since 2012, served as Area Chair for NIPS (2012, 2014, 2016, 2017) and ICCV (2012) and in the program committee of many top-tier international conferences in machine learning and computer vision. He is member of the Electrical Engineering Doctoral Program Committee at EPFL, and was or is expert for multiple funding agencies (Swiss National Science Foundation, European Research Council, Austrian Science Fund, Netherlands Organization for Scientific Research, French National Research Agency, Research Council of the Academy of Finland, US National Science Foundation).

Research Intrest

Machine learning, with a particular focus on computational aspects and small sample learning, and applications in computer vision.

List of Publications
J Newling, F Fleuret (2016)Fast mini-batch k-means by nesting. In Proceedings of the international conference on Neural Information Processing Systems (NIPS), pages 1352–1360.
F. Fleuret(2016) Predicting the dynamics of 2d objects with a deep residual network.
S Abbasi-Sureshjani, B Dasht Bozorg, BM ter Haar Romeny, and F Fleure (2017) Boosted Exudate Segmentation in Retinal Images using Residual Nets. In Proceedings of the MICCAI Workshop on Ophthalmic Medical Image Analysis (OMIA)(To appear).
A. Maksai, X. Wang, F. Fleuret, and P. Fua (2017) Non-Markovian Globally Consistent Multi-Object Tracking. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017. (To appear).
P. Baqué, F. Fleuret, and P. Fua(2017) Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection. In Proceedings of the IEEE International Conference on Computer Vision (ICCV)(To appear).
S. Tulyakov, A. Ivanov, and F. Fleuret. Weakly Supervised Learning of Deep Metrics for Stereo Reconstruction. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017. (To appear).

Global Scientific Words in Engineering