Uncertainty Quantification Machine Learning

May 24, 2026
Media Summary: 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 ... In this SEI Podcast, Dr. Eric Heim, a senior

Uncertainty Quantification Machine Learning - Detailed Analysis & Overview

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 ... In this SEI Podcast, Dr. Eric Heim, a senior A quick 20 min introduction to various UQ methods for Deep 2025 ML Academy & Artiste Distinguished Lecture. Presented at the Argonne Training Program on Extreme-Scale Computing 2019. Slides for this presentation are available here: ...

This is a quick video brief on a new paper published by Ni Zhan and myself on Speaker: Professor Eyke Hüllermeier (LMU) Titel: Presented by Lalitha Venkataramanan, Scientific Advisor at Schlumberger. Abstract: Deep IMA Data Science Seminar Speaker: Guannan Zhang (Oak Ridge National Laboratory) "Generative NYU CUSP's Research Seminar Series features leading voices in the growing field of urban informatics. Check out upcoming ... Slides and data sets available at: Recordings and video ...

Photo Gallery

Quantifying the Uncertainty in Model Predictions
Mojtaba Farmanbar - Uncertainty quantification: How much can you trust your machine learning model?
Easy introduction to gaussian process regression (uncertainty models)
Uncertainty Quantification in Machine Learning: Measuring Confidence in Predictions
Introduction to Uncertainty Quantification for Deep Learning
Uncertainty Quantification & Machine Learning
Uncertainty Quantification and Deep Learning ǀ Elise Jennings, Argonne National Laboratory
What is Uncertainty Quantification (UQ)?
Uncertainty quantification in machine learning and nonlinear least squares regression models
AIC: Uncertainty Quantification in Machine Learning: From Aleatoric to Epistemic
Uncertainty (Aleatoric vs Epistemic) | Machine Learning
Lalitha Venkataramanan: "Uncertainty Quantification in Machine Learning" | IACS Seminar

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