Department of Mathematics

Applied Mathematics

  •  Zuowei Shen, National University of Singapore
  •  Deep Approximation via Deep Learning
  •  01/28/2021
  •  3:30 AM - 4:30 AM
  •  Online (virtual meeting) (Virtual Meeting Link)
  •  Olga Turanova (

The primary task of many applications is approximating/estimating a function through samples drawn from a probability distribution on the input space. The deep approximation is to approximate a function by compositions of many layers of simple functions, that can be viewed as a series of nested feature extractors. The key idea of deep learning network is to convert layers of compositions to layers of tuneable parameters that can be adjusted through a learning process, so that it achieves a good approximation with respect to the input data. In this talk, we shall discuss mathematical theory behind this new approach and approximation rate of deep network; how this new approach differs from the classic approximation theory, and how this new theory can be used to understand and design deep learning network.



Department of Mathematics
Michigan State University
619 Red Cedar Road
C212 Wells Hall
East Lansing, MI 48824

Phone: (517) 353-0844
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College of Natural Science