Cargando…
On the transparency of large AI models
Scientists using or developing large AI models face special challenges when trying to publish their work in an open and reproducible manner. In this editorial, our journal shares some tips to help researchers in this field understand our current policies and prepare submissions that are as transpare...
Autores principales: | Wang, Wanying, Wang, Ge, Marivate, Vukosi, Hufton, Andrew L. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10382960/ https://www.ncbi.nlm.nih.gov/pubmed/37521049 http://dx.doi.org/10.1016/j.patter.2023.100797 |
Ejemplares similares
-
Unsupervised Anomaly Detection of Healthcare Providers Using Generative Adversarial Networks
por: Naidoo, Krishnan, et al.
Publicado: (2020) -
A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application
por: Mokoatle, Mpho, et al.
Publicado: (2023) -
Discriminatory Gleason grade group signatures of prostate cancer: An application of machine learning methods
por: Mokoatle, Mpho, et al.
Publicado: (2022) -
Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study
por: Oladeji, Olubusola, et al.
Publicado: (2021) -
Staying the course
por: Hufton, Andrew L.
Publicado: (2023)