Vorrichtung und Verfahren zum Bestimmen einer Unsicherheit eines von einem Generativen Maschinenlernsystem Synthetisierten Sensorsignals

Details

Veröffentlichungs-Nr.

EP4800606

Anmeldetag

26. Februar 2025

Veröffentlichung

2. September 2026

Rechtsraum

EP

IPC

G06N3/0475

Abstract

Computer-implemented method (900) for determining an uncertainty (u) of a sensor signal (x1,x2 ,xM) synthesized by a generative machine learning system (61) with respect to how likely it is to observe the sensor signal (x1 ,x2 ,xM) in physical reality comprising the steps of: • Obtaining (901) a noise sample (z); • Performing (902) Bayesian Inference on the generative machine learning (61) system using the noise sample (z) as input to the generative machine learning system (61) thereby determining a posterior predictive distribution of the sensor signal that would have been synthesized by the generative machine learning system (61) using the noise sample (z) as input to the generative machine learning system (61); • Providing (903) a measure of variability of the posterior predictive distribution as uncertainty with respect to a sensor signal (x1,x2 ,xM) synthesized from the noise sample (z), wherein the method is characterized in the posterior predictive distribution characterizing a distribution of latent features (e1 ,e2 ,eM) of the sensor signal (x1,x2 ,xM) that would have been generated using the noise sample (z) as input.

Anmelder

Firma

Robert Bosch GmbH

Land

🇩🇪 Deutschland

Fachgebiete