IACS Seminar: “Uncertainty Quantification in Machine Learning“ 2/7/20

Presented by Lalitha Venkataramanan, Scientific Advisor at Schlumberger. Abstract: Deep learning techniques have been shown to be extremely effective for various classification and regression problems, but quantifying the uncertainty of their predictions and separating them into the epistemic and aleatoric fractions is still considered challenging. In subsurface characterization projects, tools consisting of seismic, sonic, magnetic resonance, resistivity, dielectric and/or nuclear sensors are sent do