Embedding and Approximation Theorems for Echo State Networks


In this talk, I will shamelessly repeat a presentation I gave at a conference in 2020 about Echo State Networks and Dynamical Systems. Envision a room M full of objects, that evolve according to a system of ODEs ϕ. A protagonist (who is an Echo State Neural Network) stands outside the room, partially observing the objects' dynamics through a narrow window. With these observations alone, can the protagonist learn the dynamics of the objects in the room, and predict their future trajectory?

Mar 4, 2021 10:15 AM
Bath Postgraduate Student Seminar