Deep Approximation via Deep Learning
- A+
：Zuowei Shen (National University of Singapore)
： 2024-12-12 16:30
：Conference Room S103 at Experiment Building at Haiyun Campus
Speaker：Zuowei Shen (National University of Singapore)
Time：2024-12-12, 16:30
Location：Conference Room S103 at Experiment Building at Haiyun Campus
Abstract:
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; we will also show how this new approach differs from the classic approximation theory, and how this new theory can be used to understand and design deep learning networks.
