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DTSTART;TZID=America/New_York:20230412T123000
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DTSTAMP:20260411T234825
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SUMMARY:Unexpected Uses of Neural Networks: Field Theory and Metric Flows  
DESCRIPTION:Speaker: James Halverson (Northeastern University)\n \nTitle: Unexpected Uses of Neural Networks: Field Theory and Metric Flows\nAbstract:  We are now quite used to the idea that deep neural networks may be trained in a variety of ways to tackle cutting-edge problems in physics and mathematics\, sometimes leading to rigorous results. In this talk\, however\, I will argue that breakthroughs in deep learning theory are also useful for making progress\, focusing on applications to field theory and metric flows. Specifically\, I will introduce a neural network approach to field theory with a different statistical origin\, that exhibits generalized free field behavior at infinite width and interactions at finite width\, and that allows for the study of symmetries via the study of correlation functions in a different duality frame. Then\, I will review recent progress in approximating Calabi-Yau metrics as neural networks and cast that story into the language of neural tangent kernel theory\, yielding a theoretical understanding of neural network metric flows induced by gradient descent and recovering famous metric flows\, such as Perelman’s formulation of Ricci flow\, in particular limits.
URL:https://cmsa.fas.harvard.edu/event/colloquium12523/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Colloquium
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/02CMSA-Colloquium-04.12.2023.png
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DTSTART;TZID=America/New_York:20230412T153000
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CREATED:20230808T174934Z
LAST-MODIFIED:20240228T094844Z
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SUMMARY:Large deviations of Selberg’s central limit theorem
DESCRIPTION:Probability Seminar \n\nSpeaker: Emma Bailey (CUNY) \nTitle: Large deviations of Selberg’s central limit theorem \nAbstract: Selberg’s CLT concerns the typical behaviour of the Riemann zeta function and shows that the random variable $\Re \log \zeta(1/2 + i t)$\, for a uniformly drawn $t$\, behaves as a Gaussian random variable with a particular variance.  It is natural to investigate how far into the tails this Gaussianity persists\, which is the topic of this work. There are also very close connections to similar problems in circular unitary ensemble characteristic polynomials.  It transpires that a `multiscale scheme’ can be applied to both situations to understand these questions of large deviations\, as well as certain maxima and moments. In this talk I will focus more on the techniques we apply to approach this problem and I will assume no number theoretic knowledge. This is joint work with Louis-Pierre Arguin.
URL:https://cmsa.fas.harvard.edu/event/probability-41223/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Probability Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Probability-Seminar-04.12.23.png
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