Mathematics for Control

By J. Billingsley. 2005

100 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/ece1ca85c1e4ea26971dc7ef1f1e729572fbb736

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Henry W. Lin
2. Max Tegmark

Citations in the corpus

• Geometry of energy landscapes and the optimizability of deep neural networks (2018, background): “– models that comprise a sum, rather than composition, of non-linear functions – can approximate any smooth function [7].”
• Why Does Deep and Cheap Learning Work So Well? (2016, background): “This is a stronger statement than the classic universal universal approximation theorems for neural networks [3, 4], which guarantee that for every there exists some N( ), but allows for the possibility that N( ) → ∞ as → 0.”
• Discovering physical concepts with neural networks (2018, background): “For a mathematical statement of the result, see [83, 84].”