The Prairie Institute (PaRis AI Research InstitutE) is one of the four French Institutes of Artificial Intelligence, which were created as part of the national French initiative on AI announced by President Emmanuel Macron on May 29, 2018. Its 2021 Workshop will be on November 10th.
This workshop will be held on November 10th at Université Paris-Dauphine PSL (Place du Maréchal de Lattre de Tassigny, 75016 Paris), in room Raymond Aron. Please register to attend.
PROGRAMME
08:50-09:00 OPENING
Jean Ponce and Isabelle Ryl
09:00-10:40 / SESSION I
09:00-09:20 Francis Bach (Inria, PRAIRIE), “Kernel sums of squares for optimization and beyond”
09:20-09:40 Aymeric Dieuleveut (Ecole Polytechnique, Hi! Paris), “Federated Learning with compression”
09:40-10:00 Gabriel Peyré (ENS-PSL, PRAIRIE), “Scaling optimal transport for high-dimensional Learning”
10:00-10:20 Edouard Oyallon (CNRS, SCAI), “Learning is boring: image classification with patches”
10:20-10:40 Rachel Bawden (Inria, PRAIRIE), “Handling Variation in Text with Machine Translation”
10:40-11:00 Coffee break
11:00-12:40 / SESSION II
11:00-11:20 Thierry Poibeau (CNRS & ENS-PSL), “Poetry generation, around Oupoco”
11:20-11:40 Martial Hebert (Carnegie-Mellon University), “Robust AI”
11:40-12:00 Justin Carpentier (Inria, PRAIRIE), “Robotics – What should be really learned?”
12:00-12:20 Raphaël Porcher (Université Paris Cité, PRAIRIE), “Stochastic implementation of individualized treatment rules”
12:20-12:40 Nicholas Ayache (Inria, 3IA Côte-d’Azur, “AI for medical imaging – The role of models”
12:40-14:10 Lunch and posters
14:10-15:50 / SESSION III
14:10-14:30 Alexandre Gramfort (Inria, DATAIA), “Bridging the gap between neurosciences and machine learning”
14:30-14:50 Laura Cantini (CNRS, IBENS-ENS-PSL, PRAIRIE), “Single-cell multi-modal data integration”
14:50-15:10 Jean-Baptiste Masson (Institut Pasteur, PRAIRIE), “Physics-informed Bayesian learning: from random walks to fetus morphology”
15:10-15:30 Umut Simsekli (Inria, PRAIRIE), “Towards building a heavy-tailed theory of stochastic gradient descent for deep neural networks”
15:30-15:50 Julien Mairal (Inria, MIAI), “Lucas-Kanade reloaded: End-to-end super-resolution from raw image bursts”
15:50-16:10 Coffee break
16:10-17:30 / SESSION IV
16:10-16:30 Cordelia Schmid (Inria, PRAIRIE), “Do you see what I see? Large-scale learning from multimodal videos”
16:30-16:50 Jérôme Lang (Dauphine-PSL, PRAIRIE), “AI for collective decision making”
16:50-17:10 Jérôme Bolte (TSE School of Economics, ANITI), “Conservative calculus: a variational calculus for nonsmooth algorithmic differentiation”
17:10-17:30 Clément Royer (Dauphine-PSL, PRAIRIE), “Black-box optimization based on probabilistic properties”
17:30-18:00 / KEYNOTE AND CLOSURE
Yann LeCun (New York University and Facebook AI Research), “The future is self-supervised”
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