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Laurent Demanet (MIT)


Recovery phenomena with symmetric autoencoders


Is it possible to guess what a scene in an image would look like if the picture was taken from a different angle? Would it sound like you if an AI generated a deepfake of your voice? Can we find the solution of a PDE we have never seen, if we collect enough solutions of nearby equations? These questions seem to fit in a common mathematical framework of estimation of low-dimensional latent processes under maps of controlled complexity. After reviewing known results in the context of generative priors, I will explain how to formulate recovery guarantees for symmetric autoencoders using tools from applied probability and approximation theory. Joint work with Borjan Geshkovski (MIT).