Active Monitoring of Neural Networks

A. Lukina, Christian Schilling, Thomas A. Henzinger

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Neural-network classifiers are trained to achieve high prediction accuracy. However, their performance still suffers from frequently appearing inputs of unknown classes. As a component of a cyber-physical system, the classifier in this case can no longer be reliable and is typically retrained. We propose an algorithmic framework for monitoring reliability of a neural network. In contrast to static detection, a monitor wrapped in our framework operates in parallel with the classifier, communicates interpretable labeling queries to the human user, and incrementally adapts to their feedback.
Original languageEnglish
Title of host publicationBNAIC/BeneLearn 2021
Subtitle of host publication33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning
EditorsEdit Luis A. Leiva, Cédric Pruski, Réka Markovich, Amro Najjar, Christoph Schommer
Pages685-687
Publication statusPublished - 2021
Event33rd Benelux Conference on Artificial Intelligence and
30th Belgian-Dutch Conference on Machine Learning
- Esch-sur-Alzette, Luxembourg
Duration: 10 Nov 202112 Nov 2021

Conference

Conference33rd Benelux Conference on Artificial Intelligence and
30th Belgian-Dutch Conference on Machine Learning
Abbreviated titleBNAIC/BeneLearn 2021
CountryLuxembourg
CityEsch-sur-Alzette
Period10/11/2112/11/21

Keywords

  • monitoring
  • neural networks
  • novelty detection

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