A challenge in verifying a closed-loop system with a neural network controller is to be able to approximate the image of a net within a given error bound. We propose an abstract algorithm, to this end, using rational approximations for activation functions and taking advantage of Bernstein expansion. Furthermore, by exploiting monotonicity of activation functions, we propose a fast approximation that can be used for parts of the net which do not require accurate approximation for property verification.

Image Approximation for Feed Forward Neural Nets

Eleonora Pippia
;
Alberto Policriti
2020-01-01

Abstract

A challenge in verifying a closed-loop system with a neural network controller is to be able to approximate the image of a net within a given error bound. We propose an abstract algorithm, to this end, using rational approximations for activation functions and taking advantage of Bernstein expansion. Furthermore, by exploiting monotonicity of activation functions, we propose a fast approximation that can be used for parts of the net which do not require accurate approximation for property verification.
2020
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11390/1195041
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact