| DOI | Trouver le DOI : https://doi.org/10.1145/3434074.3447193 |
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| Auteur | Rechercher : Bartlett, Madeleine E.; Rechercher : Stewart, Terrence C.1; Rechercher : Thill, Serge |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | HRI '21: ACM/IEEE International Conference on Human-Robot Interaction, March 8-11, 2021, Boulder CO USA |
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| Sujet | task engagement; intensity; legendre memory units; MLP; activity recognition |
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| Résumé | In this study, we examine whether the data requirements associated with training a system to recognize multiple 'levels' of an internal state can be reduced by training systems on the 'extremes' in a way that allows them to estimate "intermediate" classes as falling in-between the trained extremes. Specifically, this study explores whether a novel recurrent neural network, the Legendre Delay Network, added as a pre-processing step to a Multi-Layer Perception, produces an output which can be used to separate an untrained intermediate class of task engagement from the trained extreme classes. The results showed that identifying untrained classes after training on the extremes is feasible, particularly when using the Legendre Delay Network. |
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| Date de publication | 2021-03-08 |
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| Maison d’édition | ACM |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 46482e71-f504-4bdf-bb24-d29f65c2c1ad |
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| Enregistrement créé | 2021-07-28 |
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| Enregistrement modifié | 2021-07-28 |
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