Methods and Procedures for the Verification and Validation of Artificial Neural Networks

By Brian J. Taylor. 2005

115 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

How researchers see this paper

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

In Artificial Intelligence

1. Wojciech Samek
2. K. Müller
3. Grgoire Montavon
4. Sebastian Lapuschkin
5. Christopher J. Anders
6. Alexander Binder
7. Longo Luca
8. Giulia Vilone

In Control and Systems Engineering

1. Grgoire Montavon
2. K. Müller
3. Stuart J. Russell
4. Wojciech Samek
5. Max Tegmark
6. Dan Dewey

Citations in the corpus

• Methods for interpreting and understanding deep neural networks (2017, background): “Techniques for interpreting and understanding what the model has learned have therefore become a key ingredient of a robust validation procedure [51, 6, 5].”
• Research Priorities for Robust and Beneficial Artificial Intelligence (2015, background): “Some work has been done on verifying neural network applications (Pulina and Tacchella 2010; Taylor 2006; Schumann and Liu 2010) and the notion of partial programs (Andre and Russell 2002; Spears 2006) allows the designer to impose arbitrary ‘‘structural’’ constraints on behavior, but much remains t”