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Neurosymbolic Learning: On Generalising Relational Visuospatial and Temporal Structure
German Aerospace Center (DLR), Germany.
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-6290-5492
2022 (English)In: Proceedings of the First International Conference on Foundations, Applications, and Theory of Inductive Logic (FATIL2022) / [ed] Matthias Thimm; Jürgen Landes; Kenneth Skiba, FernUniversität in Hagen , 2022, p. 84-86Conference paper, Published paper (Refereed)
Abstract [en]

We position ongoing research aimed at developing a general framework for structured spatio-temporal learning from multimodal human behavioural stimuli. The framework and its underlying general, modular methods serve as a model for the application of integrated (neural) visuo-auditory processing and (semantic) relational learning foundations for applications (primarily) in the behavioural sciences.

Place, publisher, year, edition, pages
FernUniversität in Hagen , 2022. p. 84-86
Keywords [en]
Representation learning and grounding, Relational learning, Cognitive vision and perception, Multimodality, Dynamic Visuospatial Imagery
National Category
Computer Sciences Computer graphics and computer vision Applied Psychology
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-101287DOI: 10.18445/20220817-161753-0OAI: oai:DiVA.org:oru-101287DiVA, id: diva2:1696419
Conference
The First International Conference on Foundations, Applications, and Theory of Inductive Logic (FATIL2022), Munich, Germany, October 12-14, 2022
Available from: 2022-09-16 Created: 2022-09-16 Last updated: 2025-02-01Bibliographically approved

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Bhatt, Mehul

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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Language
  • de-DE
  • en-GB
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  • nn-NB
  • sv-SE
  • Other locale
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Output format
  • html
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  • asciidoc
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