Cargando…
Adversarial Autoencoder and Multi-Task Semi-Supervised Learning for Multi-stage Process
In selection processes, decisions follow a sequence of stages. Early stages have more applicants and general information, while later stages have fewer applicants but specific data. This is represented by a dual funnel structure, in which the sample size decreases from one stage to the other while t...
Autores principales: | Mendes, Andre, Togelius, Julian, dos Santos Coelho, Leandro |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206279/ http://dx.doi.org/10.1007/978-3-030-47436-2_1 |
Ejemplares similares
-
MTSS-AAE: Multi-task semi-supervised adversarial autoencoding for COVID-19 detection based on chest X-ray images
por: Ullah, Zahid, et al.
Publicado: (2023) -
Generative Adversarial Training for Supervised and Semi-supervised Learning
por: Wang, Xianmin, et al.
Publicado: (2022) -
A Semi-Supervised Stacked Autoencoder Using the Pseudo Label for Classification Tasks
por: Lai, Jie, et al.
Publicado: (2023) -
Improved semi-supervised autoencoder for deception detection
por: Fu, Hongliang, et al.
Publicado: (2019) -
Semi-Supervised Learning for Defect Segmentation with Autoencoder Auxiliary Module
por: Sae-ang, Bee-ing, et al.
Publicado: (2022)