BIMSA >
Seminar on Control Theory and Nonlinear Filtering
Rectified Deep Neural Networks Overcome the Curse of Dimensionality When Approximating Solutions of McKean-Vlasov Stochastic Differential Equations
Rectified Deep Neural Networks Overcome the Curse of Dimensionality When Approximating Solutions of McKean-Vlasov Stochastic Differential Equations
Organizer
Speaker
Zeju Sun
Time
Thursday, December 21, 2023 2:30 PM - 3:00 PM
Venue
理科楼A-230
Abstract
In this talk, I will review the paper entitled ‘Rectified Deep Neural Networks Overcome the Curse of Dimensionality When Approximating Solutions of McKean-Vlasov Stochastic Differential Equations’, in which the authors prove that rectified deep neural networks do not suffer from the curse of dimensionality when approximating McKean–Vlasov SDEs in the sense that the number of parameters in the deep neural networks only grows polynomially in the space dimension d of the SDE and the reciprocal of the accuracy .