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Graphical models (or probabilistic graphical models) provide a powerful
paradigm to jointly exploit probability theory and graph theory for solving
complex real-world problems. They form an indispensable component in several
research areas, such as statistics, machine learning, computer vision, where a
graph expresses the conditional (probabilistic) dependence among random
variables.
This course will focus on discrete models, that is, cases where the random variables of the graphical models are discrete. After an introduction to the basics of graphical models, the course will then focus on state-of-the-art problems at the intersection of deep learning and graphical models. Several applications in machine learning and computer vision will be studied as part of the course. |
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All the classes will be held at the Gif-sur-Yvette campus of CentraleSupelec, unless mentioned otherwise below.
Mailing list: All announcements (last-minute changes, projects, etc.) will be made on a dedicated mailing list. If you would like to subscribe to the list, visit: https://sympa.inria.fr/sympa/subscribe/grmdil. |
| 23/1 | 13:30 - 16:45 | (EB.114, Eiffel) | Introduction, BP, st-mincut [slides] | |
| 01/2 | 17:00 - 18:30 | (online) | Variational inference [slides] | |
| 02/2 | 17:00 - 18:30 | (online) | Simulation-based inference [slides] | |
| 06/2 | 13:30 - 16:45 | (Amphi III, Eiffel) | ||
| 13/2 | 13:30 - 16:45 | (MF.206, Eiffel) | [slides (will be updated)] | |
| 05/3 | 13:30 - 16:45 | (MF.206, Eiffel) | Graph neural networks[slides] | |
| 12/3 | 13:30 - 16:45 | (Amphi III, Eiffel) | ||
| 19/3 | 13:30 - 16:45 | - | ||
| TBD | TBD | TBD | Project presentations |