Dec 22, 2019 · We build on the dynamical systems approach to deep learning, where deep residual networks are idealized as continuous-time dynamical systems, ...
Abstract. We build on the dynamical systems approach to deep learning, where deep residual networks are idealized as continuous-time dynamical systems, ...
Jun 8, 2020 · In this section, we summarize our main results on the approximation properties of Hode and discuss their significance with respect to related ...
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
Posted: Oct 3, 2022
A brief introduction. For a given function f : Rd → R and ε > 0, approximation is to find a simple function g such that f − g < ε.
Sep 14, 2022 · NUS mathematicians have developed a new theoretical framework based on dynamical systems to understand when and how a deep neural network ...
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Dec 22, 2019 · We build on the dynamical systems approach to deep learning, where deep ... Deep Learning via Dynamical Systems: An Approximation Perspective.
We build on the dynamical systems approach to deep learning, where deep residual networks are idealized as continuous-time dynamical systems.
Mar 22, 2017 · We discuss the idea of using continuous dynamical systems to model general high-dimensional nonlinear functions used in machine learning.