Exploring Optimizing Astronomical Observatories With Machine Learning And Uncertainty Quantification
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- Title:
- Presented by Lalitha Venkataramanan, Scientific Advisor at Schlumberger. Abstract:
- Presenter: James Warner (NASA Langley Research Center) Adopting
- Recorded 02 May 2023. Marcus Noack of Lawrence Berkeley Laboratory presents "Advanced Gaussian Process Function ...
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In-Depth Information on Optimizing Astronomical Observatories With Machine Learning And Uncertainty Quantification
Presented virtually at the Unconference session at the Oxford Authors: Bin Wang, Jie Lu, Zheng Yan, Huaishao Luo, Tianrui Li, Yu Zheng and Guangquan Zhang More on ... Neural networks are infamous for making wrong predictions with high confidence. Ideally, when a model encounters difficult ... Gaussian process regression (GPR) is a probabilistic approach to making predictions. GPRs are easy to implement, flexible, and ...
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