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DTSTART;TZID=America/New_York:20201104T150000
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SUMMARY:Some exactly solvable models for machine learning via Statistical physics
DESCRIPTION:Speaker: Florent Krzakala\, EPFL \nTitle: Some exactly solvable models for machine learning via Statistical physics \nAbstract: The increasing dimensionality of data in the modern machine learning age presents new challenges and opportunities. The high dimensional settings allow one to use powerful asymptotic methods from probability theory and statistical physics to obtain precise characterizations and develop new algorithmic approaches. Statistical mechanics approaches\, in particular\, are very well suited for such problems. Will give examples of recent works in our group that build on powerful methods of statistical physics of disordered systems to analyze some relevant questions in machine learning and neural networks\, including overparameterization\, kernel methods\, and the behavior gradient descent algorithm in a high dimensional non-convex landscape.
URL:https://cmsa.fas.harvard.edu/event/11-4-2020-new-technologies-in-math/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-New-Technologies-in-Mathematics-11.04.20.png
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DTSTART;TZID=America/New_York:20201111T150000
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DTSTAMP:20240515T200604Z
CREATED:20240127T021159Z
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SUMMARY:Towards AI for mathematical modeling of complex biological systems: Machine-learned model reduction\, spatial graph dynamics\, and symbolic mathematics
DESCRIPTION:Speaker: Eric Mjolsness\, Departments of Computer Science and Mathematics\, UC Irvine \nTitle: Towards AI for mathematical modeling of complex biological systems: Machine-learned model reduction\, spatial graph dynamics\, and symbolic mathematics \nAbstract: The complexity of biological systems (among others) makes demands on the complexity of the mathematical modeling enterprise that could be satisfied with mathematical artificially intelligence of both symbolic and numerical flavors. Technologies that I think will be fruitful in this regard include (1) the use of machine learning to bridge spatiotemporal scales\, which I will illustrate with the “Dynamic Boltzmann Distribution” method for learning model reduction of stochastic spatial biochemical networks and the “Graph Prolongation Convolutional Network” approach to course-graining the biophysics of microtubules; (2) a meta-language for stochastic spatial graph dynamics\, “Dynamical Graph Grammars”\, that can represent structure-changing processes including microtubule dynamics and that has an underlying combinatorial theory related to operator algebras; and (3) an integrative conceptual architecture of typed symbolic modeling languages and structure-preserving maps between them\, including model reduction and implementation maps. \n  \n  \n 
URL:https://cmsa.fas.harvard.edu/event/11-11-2020-new-technologies-in-mathematics/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-New-Technologies-in-Mathematics-11.11.20-1.png
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201118T150000
DTEND;TZID=America/New_York:20201118T160000
DTSTAMP:20240515T200420Z
CREATED:20240127T020145Z
LAST-MODIFIED:20240515T200420Z
UID:10001472-1605711600-1605715200@cmsa.fas.harvard.edu
SUMMARY:Universes as Big Data\, or Machine-Learning Mathematical Structures
DESCRIPTION:Speaker: Yang-Hui He\, Oxford University\, City University of London and Nankai University \nTitle: Universes as Big Data\, or Machine-Learning Mathematical Structures \nAbstract: We review how historically the problem of string phenomenology lead theoretical physics first to algebraic/differetial geometry\, and then to computational geometry\, and now to data science and AI. With the concrete playground of the Calabi-Yau landscape\, accumulated by the collaboration of physicists\, mathematicians and computer scientists over the last 4 decades\, we show how the latest techniques in machine-learning can help explore problems of physical and mathematical interest\, from geometry\, to group theory\, to combinatorics and number theory. \n  \n 
URL:https://cmsa.fas.harvard.edu/event/11-18-2020-new-tech-in-math/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-New-Technologies-in-Mathematics-11.18.20.png
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