Learning Numerosity Representations with Transformers: Number Generation Tasks and Out-of-Distribution Generalization

One of the most rapidly advancing areas of deep learning research aims at creating models that learn to disentangle the latent factors of variation from a data distribution. However, modeling joint probability mass functions is usually prohibitive, which motivates the use of conditional models assum...

Descripción completa

Detalles Bibliográficos
Autores principales: Boccato, Tommaso, Testolin, Alberto, Zorzi, Marco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8303966/
https://www.ncbi.nlm.nih.gov/pubmed/34356398
http://dx.doi.org/10.3390/e23070857

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8303966/
https://www.ncbi.nlm.nih.gov/pubmed/34356398
http://dx.doi.org/10.3390/e23070857

Ejemplares similares