Cargando…
Learning to sense from events via semantic variational autoencoder
In this paper, we introduce the concept of learning to sense, which aims to emulate a complex characteristic of human reasoning: the ability to monitor and understand a set of interdependent events for decision-making processes. Event datasets are composed of textual data and spatio-temporal feature...
Autores principales: | Gôlo, Marcos Paulo Silva, Rossi, Rafael Geraldeli, Marcacini, Ricardo Marcondes |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699685/ https://www.ncbi.nlm.nih.gov/pubmed/34941880 http://dx.doi.org/10.1371/journal.pone.0260701 |
Ejemplares similares
-
A network-based positive and unlabeled learning approach for fake news detection
por: de Souza, Mariana Caravanti, et al.
Publicado: (2021) -
Cross-modal semantic autoencoder with embedding consensus
por: Sun, Shengzi, et al.
Publicado: (2021) -
Optimizing Few-Shot Learning Based on Variational Autoencoders
por: Wei, Ruoqi, et al.
Publicado: (2021) -
Variational autoencoders learn transferrable representations of metabolomics data
por: Gomari, Daniel P., et al.
Publicado: (2022) -
Accurate Tumor Subtype Detection with Raman Spectroscopy
via Variational Autoencoder and Machine Learning
por: He, Chang, et al.
Publicado: (2022)