Jun 13, 2019
The Approximation Power of Deep Neural Networks: Theory and Applications
Date: June 13, 2019 |
4:00 pm –
Speaker: Gitta Kutyniok, TU Berlin
Location: Big Seminar room Ground floor / Office Bldg West (I21.EG.101)
In this talk we will provide an introduction into this research area. After a general overview of mathematics of deep neural networks, we will discuss theoretical results which prove that not only do (memory-optimal) neural networks have as much approximation power as classical systems such as wavelets or shearlets, but they are also able to beat the curse of dimensionality. On the numerical side, we will then show that superior performance can typically be achieved by combining deep neural networks with classical approaches from approximation theory.