Dec 22, 2019 · We build on the dynamical systems approach to deep learning, where deep residual networks are idealized as continuous-time dynamical systems, ...

We build on the dynamical systems approach to deep learning, where deep residual networks are idealized as continuous-time dynamical systems, from the ...

We build on the dynamical systems approach to deep learning, where deep residual networks are idealized as continuous-time dynamical systems, ...

In this paper, we establish some basic results on the approximation mechanism of composition, building on the dynamical systems approach to deep learning, where ...

Duration: 44:54
Posted: Oct 3, 2022

Dec 22, 2019 · This paper provides a general sufficient condition for a residual network to have the power of universal approximation.

Sep 14, 2022 · NUS mathematicians have developed a new theoretical framework based on dynamical systems to understand when and how a deep neural network can learn arbitrary ...

From the perspective of approximation theory, both residual networks and neural ODEs are universal approximators [25,34,35,24,32,31].

Linear approximation provides a good approximation for smooth functions. 2. Advantage: It is a good approximation scheme for d is small, domain is simple, ...

Sep 29, 2022 · Shen, “Deep learning via dynamical systems: An approximation perspective,” Journal of the European Mathematical Society, 2022. 2. Q. Li, T ...