Zobrazeno 1 - 2
of 2
pro vyhledávání: '"Alfredo Nazabal"'
Publikováno v:
Nazábal, A, Tsagkas, N & Williams, C K I 2023, ' Inference and Learning for Generative Capsule Models ', Neural Computation, vol. 35, no. 4, pp. 727-761 . https://doi.org/10.1162/neco_a_01564
Capsule networks (see e.g. Hinton et al., 2018) aim to encode knowledge of and reason about the relationship between an object and its parts. In this paper we specify a generative model for such data, and derive a variational algorithm for inferring
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1627dfcea9632dd31692e9ac59141237
https://hdl.handle.net/20.500.11820/d547617c-d731-4225-abb9-6b5963368b97
https://hdl.handle.net/20.500.11820/d547617c-d731-4225-abb9-6b5963368b97
Autor:
Tomas Petricek, Gerrit J.J. van den Burg, Alfredo Nazabal, Taha Ceritli, Ernesto Jimenez-Ruiz, Christopher K. I. Williams
Publikováno v:
Petricek, T, van Den Burg, G J J, Nazábal, A, Ceritli, T, Jiménez-Ruiz, E & Williams, C K I 2022, ' AI Assistants: A Framework for Semi-Automated Data Wrangling ', IEEE Transactions on Knowledge and Data Engineering . https://doi.org/10.1109/TKDE.2022.3222538
Data wrangling tasks such as obtaining and linking data from various sources, transforming data formats, and correcting erroneous records, can constitute up to 80% of typical data engineering work. Despite the rise of machine learning and artificial
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::befb89110bc833134ff82adf3228e7d0
https://doi.org/10.1109/tkde.2022.3222538
https://doi.org/10.1109/tkde.2022.3222538
