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Neural Stochastic Differential Equations with Neural Processes Family Members for Uncertainty Estimation in Deep Learning
Existing neural stochastic differential equation models, such as SDE-Net, can quantify the uncertainties of deep neural networks (DNNs) from a dynamical system perspective. SDE-Net is either dominated by its drift net with in-distribution (ID) data to achieve good predictive accuracy, or dominated b...
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| Publié dans: | Sensors (Basel) |
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| Auteurs principaux: | , |
| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
MDPI
2021
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| Sujets: | |
| Accès en ligne: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8197858/ https://ncbi.nlm.nih.gov/pubmed/34073566 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/s21113708 |
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