Using Machine Learning Uncertainty Quantification

Media Summary: Neural networks are infamous for making wrong predictions Gaussian process regression (GPR) is a probabilistic approach to making predictions. GPRs are easy to implement, flexible, and ... Presented at the Argonne Training Program on Extreme-Scale Computing 2019. Slides for this presentation are available here: ...

Using Machine Learning Uncertainty Quantification - Detailed Analysis & Overview

Neural networks are infamous for making wrong predictions Gaussian process regression (GPR) is a probabilistic approach to making predictions. GPRs are easy to implement, flexible, and ... Presented at the Argonne Training Program on Extreme-Scale Computing 2019. Slides for this presentation are available here: ... 2025 ML Academy & Artiste Distinguished Lecture. Pau is a PhD student in Computing and Mathematical Sciences at Caltech, advised by Houman Owhadi. His main research area ... Predictions from modeling and simulation (M&S) are increasingly relied upon to inform critical decision making in a variety of ...

In this SEI Podcast, Dr. Eric Heim, a senior IMA Data Science Seminar Speaker: Guannan Zhang (Oak Ridge National Laboratory) "Generative Presented by Lalitha Venkataramanan, Scientific Advisor at Schlumberger. Abstract: Deep Speaker: Professor Eyke Hüllermeier (LMU) Titel: A quick 20 min introduction to various UQ methods for Deep NYU CUSP's Research Seminar Series features leading voices in the growing field of urban informatics. Check out upcoming ...

Presenter: James Warner (NASA Langley Research Center) Adopting

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Using Machine Learning Uncertainty Quantification