Some thoughts on knowledge-enhanced machine learning

Fabio Gagliardi Cozman, Hugo Neri Munhoz

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

1 Cita (Scopus)

Resumen

How can we employ theoretical insights and practical tools from knowledge representation and reasoning to enhance machine learning, and when is it worthwhile to do so? This paper is based on an invited talk delivered at ECSQARU2019 around this question. It emphasizes the knowledge representation and reasoning side of knowledge-enhanced machine learning, looking at a few case studies: the finite model theory of probabilistic languages, the generation of explanations for embeddings, and an “explainable” version of the Winograd Challenge.

Idioma originalInglés
Páginas (desde-hasta)308-324
Número de páginas17
PublicaciónInternational Journal of Approximate Reasoning
Volumen136
DOI
EstadoPublicada - sep 2021
Publicado de forma externa

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