Cargando…
Characterizing Uncertainty in Machine Learning for Chemistry
[Image: see text] Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings...
Autores principales: | Heid, Esther, McGill, Charles J., Vermeire, Florence H., Green, William H. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
|
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10336963/ https://www.ncbi.nlm.nih.gov/pubmed/37338239 http://dx.doi.org/10.1021/acs.jcim.3c00373 |
Ejemplares similares
-
Machine Learning of Reaction Properties via Learned
Representations of the Condensed Graph of Reaction
por: Heid, Esther, et al.
Publicado: (2021) -
20th Conference on the Physics and Chemistry of Semiconductor Interfaces
por: McGill, T C
Publicado: (1993) -
Machine learning in chemistry
por: Pyzer-Knapp, Edward O, et al.
Publicado: (2019) -
Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors
por: Guan, Yanfei, et al.
Publicado: (2020) -
Emerging Chemistry & Machine Learning
por: Jones, Christopher W., et al.
Publicado: (2022)