[Seminars] Fwd: [comp-in-sci] Leveraging physical models in machine learning, Rebecca Willett

[David W. Miller]

Hi all,

Please consider joining this week’s Computations in Science seminar by our CS colleague Rebecca Willett on Leveraging physical models in machine learning. Should be great!

Cheers,
David

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From: <strong1 at uchicago.edu<mailto:strong1 at uchicago.edu>>
Date: Mon, Nov 18, 2019 at 9:22 AM
Subject: [comp-in-sci] Leveraging physical models in machine learning, Rebecca Willett
To: <comp-in-sci at lists.uchicago.edu<mailto:comp-in-sci at lists.uchicago.edu>>


COMPUTATIONS IN SCIENCE SEMINAR
Nov 2019
20
Wed 12:15
Rebecca Willett, University of Chicago
Leveraging physical models in machine learning

Machine learning, at its heart, is the process of learning from examples. However, in many scientific domains, we not only have training data or examples from which to learn, but also physical models of either the data collection mechanism or the underlying physical phenomenon. In this talk, I will describe two settings in which physical models can be incorporated within a machine learning framework to yield improved predictive performance. First, we will consider using training data to help solve ill-posed linear inverse problem such as deblurring, deconvolution, inpainting, compressed sensing, and superresolution. Recent advances in machine learning and image processing have illustrated that it is often possible to learn a regularizer from training data that can outperform more traditional regularizers. We will see that whether or how a forward model is leveraged can significantly impact how many training samples are needed to achieve a target accuracy. Second, we will examine using a combination of observational data and simulated data to improve subseasonal climate forecasts. Treating both types of data as co-equal training samples can bias many learning methods and yield misleading results. I will describe an alternative framework that combines observational data with a correlation graph that can be estimated from large ensemble climate model outputs, and we will see how this approach leads to more accurate forecasts. Finally, we will discuss open problems and future directions at the intersection of machine learning and the physical sciences.

Wednesday, November 20, 2019. KPTC 206, 12:15
(Discussion over bag-lunch at 12:00)
UPCOMING TALKS
December 04, 2019: Irmgard Bischofberger, MIT.
December 11, 2019: W. Benjamin Rogers, Brandeis University.
January 08, 2020: Bob Rosner, University of Chicago.
For more info: mrsec.uchicago.edu/Comp_in_Sci<http://mrsec.uchicago.edu/Comp_in_Sci>

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