[HDI Lab seminar] Approximation with deep neural networks

In recent years, there has been quite a number of new results on approximation properties of deep neural networks. This emergent theory is rather different from the classical approximation theory of linear or shallow models (such as splines or Fourier expansion). In this talk, I will try to highlight some interesting ideas of this theory, and will also briefly indicate its connections to other topics such as information theory, dynamical systems, fewnomials and VC dimension. Speaker: Dmitry Yarotsky, Skoltech March 2, 2021 HDI Lab: Faculty of Computer Science: Facebook: Twitter: