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DTSTART:20260329T010000
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DTSTART;TZID=Europe/Paris:20260409T124500
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CREATED:20260326T102406Z
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SUMMARY:ENS-Data Science colloquium - Michael Chertkov : Samples That Cooperate\, Samples That Remember: Two Exactly Solvable Bridge Diffusions
DESCRIPTION:Diffusion-based generative models treat samples as independent and memoryless. I will show that relaxing each assumption leads to rich\, exactly solvable physics — with no neural networks anywhere.Giving samples a present — coupling them through their evolving mean field — produces a McKean–Vlasov optimal transport problem whose self-consistent guidance is provably the linear interpolant between endpoint means\, for arbitrary distributions and any interaction schedule; applied to building-fleet demand response\, this saves 20%+ in actuation energy.Giving samples a past produces a continual-learning agent whose memory is a Bridge Diffusion and whose forgetting — arising from a single lossy temporal coarse-graining step — obeys a universal linear capacity law with a Shannon-like constant.Both constructions live in the world of Riccati equations\, hyperbolic functions\, and mixture linear algebra; the physics of the bridge — not the expressivity of a network — controls what is achievable. \n\n\n\n  \n\n\n\nThese seminars are being made possible through the support of the CFM-ENS Chair « Modèles et Sciences des Données ». \n\n\n\nThe organizers: Giulio Biroli\, Alex Cayco Gajic\, Bruno Loureiro\, Stéphane Mallat\, Gabriel Peyré.
URL:https://www.math.ens.psl.eu/evenement/ens-data-science-colloquium-michael-chertkov-samples-that-cooperate-samples-that-remember-two-exactly-solvable-bridge-diffusions/
LOCATION:ENS Salle Dussane
CATEGORIES:ENS-Data Science colloquium
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