Abstract
ABSTRACT Neural networks play a central role in the construction of learning models for artificial intelligence and machine learning. This is because neural networks are highly flexible and can approximate a wide variety of maps with high accuracy. The flexibility of neural networks is theoretically guaranteed by using universal approximation theorems. When the input and output spaces have finite dimensions, the universal approximation property of neural networks has been intensively investigated under various conditions of width, depth, and activation functions. However, these finite-dimensional results cannot be directly applied to settings in which an input or output space has infinite dimensions, such as functional data analysis and neural processes. This study provides a universal approximation theorem with neural networks for uniformly continuous maps between function spaces, whose dimensions can be infinite.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Experimental & Theoretical Artificial Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
