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X-WR-CALDESC:Events for IFDS
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DTSTART;TZID=America/Chicago:20220307T123000
DTEND;TZID=America/Chicago:20220307T133000
DTSTAMP:20260409T225600
CREATED:20220119T200618Z
LAST-MODIFIED:20220308T155114Z
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SUMMARY:IFDS Ideas Forum: Multiscale inverse problem\, from Schroedinger to Newton to Boltzmann
DESCRIPTION:Title: Multiscale inverse problem\, from Schroedinger to Newton to Boltzmann \nSpeaker: Qin Li\, Department of Mathematics \nDate + Location: 7 March (Monday)\, Orchard View Room \nAbstract: Inverse problems are ubiquitous. People probe the media with sources and measure the outputs. At the scale of quantum\, classical\, statistical and fluid\, these are inverse Schroedinger\, inverse Newton’s second law\, inverse Boltzmann problem\, and inverse diffusion respectively. The universe\, however\, should have a universal mathematical description\, as Hilbert proposed in 1900. In this talk\, we present a line of research results that unify all these inverse problems. Facing the IFDS crowd\, I’d like to ask the following question: how to integrate the mathematical equivalence into the optimization formulation for a more efficient algorithmic pipeline than the traditional PDE-constrained optimization? \nBio: Qin Li is an associate professor of mathematics at UW-Madison. Her research lies between scientific computing and PDE-constrained optimization.
URL:https://ifds.info/event/ifds-ideas-forum-some-ill-conditioned-and-well-conditioned-inverse-problems/
LOCATION:Orchard View Room\, 330 N. Orchard Street\, 3rd Floor NE\, Madison\, Wisconsin\, 53715\, United States
CATEGORIES:IFDS Ideas Forum
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20220321T123000
DTEND;TZID=America/Chicago:20220321T133000
DTSTAMP:20260409T225600
CREATED:20220119T200618Z
LAST-MODIFIED:20220308T161728Z
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SUMMARY:IFDS Ideas Forum: How to Make the Gradients Small in Convex and Min-Max Optimization
DESCRIPTION:Speaker: Jelena Diakonikolas
URL:https://ifds.info/event/ifds-ideas-forum-03212022/
LOCATION:Orchard View Room\, 330 N. Orchard Street\, 3rd Floor NE\, Madison\, Wisconsin\, 53715\, United States
CATEGORIES:IFDS Ideas Forum
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20220328T123000
DTEND;TZID=America/Chicago:20220328T133000
DTSTAMP:20260409T225600
CREATED:20220119T200618Z
LAST-MODIFIED:20220411T163036Z
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SUMMARY:IFDS Ideas Forum: A Review of Neural Collapse
DESCRIPTION:Speaker: Greg Canal \nAbstract: Neural Collapse is a recently discovered phenomenon in deep neural network training that describes class separation in the final network layers. When a classification network is trained past the point of zero training error\, it has been observed that the penultimate layer activations collapse to their respective class means\, the means themselves form a simplex equiangular tight frame\, and the final layer linear classifiers align with each respective mean. Neural collapse has been demonstrated both empirically on deep networks as well as theoretically on simplified models\, and has inspired new questions on generalization\, robustness\, and architecture design. In this talk I will review the original discovery of neural collapse\, as well as recent literature that expands on related questions.
URL:https://ifds.info/event/ifds-ideas-forum-03282022/
LOCATION:Orchard View Room\, 330 N. Orchard Street\, 3rd Floor NE\, Madison\, Wisconsin\, 53715\, United States
CATEGORIES:IFDS Ideas Forum
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