In the latest years we have seen an increased us of machine learning applications due to
the increasing computational power and the development of more advanced techniques to …

The paper presents and evaluates the design and the implementation of a self-checking
neural system for photon event identification in intensified charge-coupled device detectors …

This chapter presents a general methodology for evaluating the loss in performance of a
generic neural network once its weights are affected by perturbations. Since weights …

Abstract As Machine Learning (ML) has seen increasing adoption in safety-critical domains
(eg, autonomous vehicles), the reliability of ML systems has also grown in importance. While …

This paper compares the sensitivity definitions of neural networks' output to input and weight
perturbations. Based on the essence of the sensitivity definitions, the authors classify these …

The paper provides a methodology for evaluating the performance degradation of a
feedforward neural network once affected by fixed perturbations injected in the computation …

HPC systems are widely used in industrial, economical, and scientific applications, and
many of these applications are safety-and time-critical. We must ensure that the application …

The recent success of deep neural networks (DNNs) in challenging perception tasks makes
them a powerful tool for robotic applications in open context environments, such as …

The problem of quality model creation for models based on decision trees and forests is
considered. The set of indicators characterizing properties of decision trees and forests is …

The paper quantifies the degradation in performance of a linear model induced by
perturbations affecting its identified parameters. We extend sensitivity analyses available in …