Learning fine-grained estimation of physiological states from coarse-grained labels by distribution restoration
Abstract Due to its importance in clinical science, the estimation of physiological states (e.g., the severity of pathological tremor) has aroused growing interest in machine learning community. While the physiological state is a continuous variable, its continuity is lost when the physiological sta...
Guardado en:
Autores principales: | Zengyi Qin, Jiansheng Chen, Zhenyu Jiang, Xumin Yu, Chunhua Hu, Yu Ma, Suhua Miao, Rongsong Zhou |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2020
|
Materias: | |
Acceso en línea: | https://doaj.org/article/e942291820ca48d59ab2991c456caf30 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks.
por: Andrea Tangherloni, et al.
Publicado: (2021) -
Machine learning coarse grained models for water
por: Henry Chan, et al.
Publicado: (2019) -
Backmapping triangulated surfaces to coarse-grained membrane models
por: Weria Pezeshkian, et al.
Publicado: (2020) -
Protein–ligand binding with the coarse-grained Martini model
por: Paulo C. T. Souza, et al.
Publicado: (2020) -
Ultrasonic Welding of Nickel with Coarse and Ultrafine Grained Structures
por: Elvina R. Shayakhmetova, et al.
Publicado: (2021)