Section: Other Grants and Activities
Agence Nationale de la Recherche: MGA (INRIA/ENPC)
Participants : Jean-Yves Audibert, Francis Bach, Olivier Duchenne, Julien Mairal, Jean Ponce, Andrew Zisserman.
Probabilistic graphical models, also known as Bayesian Networks, provide a very flexible and powerful framework for capturing statistical dependencies in complex, multivariate data. They enable the building of large global probabilistic models for complex phenomena out of smaller and more tractable local models. The objectives of this project are to advance the methodological state of the art of probabilistic modeling research, while applying the newly developed techniques to computer vision, text processing and bio-informatics. F. Bach is the coordinator of this ANR “projet blanc” in machine learning, that focuses on graphical models and their applications. The total funding is 200 KEuros, with 100KEuros for Willow including (50KEuros for INRIA and 50KEuros for ENPC).