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DTSTART:20220101T000000
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DTSTART;TZID=UTC:20220222T110000
DTEND;TZID=UTC:20220222T120000
DTSTAMP:20260414T150134
CREATED:20220214T125321Z
LAST-MODIFIED:20220214T125321Z
UID:6605-1645527600-1645531200@aarms.math.ca
SUMMARY:AARMS Scientific Machine Learning Seminar:  Alison Malcom (Memorial University)
DESCRIPTION:Using Normalizing Flows for Seismic Data Interpolation\nNormalizing Flows are a type of neural network that allow us to map one probability distribution into another.  The advantage of such a technique is that they allow us to relate a simple distribution\, like a Gaussian\, to a more complicated distribution that may be more difficult to estimate and sample from.  In uncertainty quantification for inverse problems\, we are trying to estimate one of these more complicated distributions\, thus normalizing flows can help to speed up this process and improve our ability to use and analyze our results.  This will be an applied talk\, giving an introduction to normalizing flows\, discussing how we chose the particular machine learning method and explaining how it improves our understanding of seismic data processing and the associated uncertainties.
URL:https://aarms.math.ca/event/aarms-scientific-machine-learning-seminar-alison-malcom-memorial-university/
LOCATION:WebEx seminar
CATEGORIES:AARMS Scientific Machine Learning Seminar
ORGANIZER;CN="Alexander%20Bihlo":MAILTO:abihlo@mun.ca
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