Cargando…
Click-through Rate Prediction and Uncertainty Quantification Based on Bayesian Deep Learning
Click-through rate (CTR) prediction is a research point for measuring recommendation systems and calculating AD traffic. Existing studies have proved that deep learning performs very well in prediction tasks, but most of the existing studies are based on deterministic models, and there is a big gap...
Autores principales: | Wang, Xiaowei, Dong, Hongbin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10048037/ https://www.ncbi.nlm.nih.gov/pubmed/36981295 http://dx.doi.org/10.3390/e25030406 |
Ejemplares similares
-
Bayesian uncertainty quantification for data-driven equation learning
por: Martina-Perez, Simon, et al.
Publicado: (2021) -
Uncertainty quantification for Bayesian active learning in rupture life prediction of ferritic steels
por: Mamun, Osman, et al.
Publicado: (2022) -
Explainable uncertainty quantifications for deep learning-based molecular property prediction
por: Yang, Chu-I, et al.
Publicado: (2023) -
Evaluation of Uncertainty Quantification in Deep Learning
por: Ståhl, Niclas, et al.
Publicado: (2020) -
On the Quantification of Model Uncertainty: A Bayesian Perspective
por: Kaplan, David
Publicado: (2021)