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DTSTART;TZID=America/New_York:20210406T090000
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DTSTAMP:20260702T104754
CREATED:20230707T171215Z
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SUMMARY:Memorial Conference for the founders of index theory: Atiyah\, Bott\, Hirzebruch\, and Singer
DESCRIPTION:In 2021\, the CMSA hosted a lecture series on the literature of the mathematical sciences. This series highlights significant accomplishments in the intersection between mathematics and the sciences. Speakers include Edward Witten\, Lydia Bieri\, Simon Donaldson\, Michael Freedman\, Dan Freed\, and many more. \nVideos of these talks can be found in this Youtube playlist. \n \nIn the Spring 2021 semester\, the CMSA hosted a sub-program on this series titled A Memorial Conference for the founders of index theory: Atiyah\, Bott\, Hirzebruch and Singer. Below is the schedule for talks in that subprogram \nApril 6\, 2021 | 9:00 – 10:30am ET\n\n\n\n\nEdward Witten (IAS) \nTitle: Isadore Singer’s Work on Analytic Torsion\n\n\n\n\n\nApril 13\, 2021 | 9:00 – 10:30am ET\n\n\n\n\nClaire Voisin (College de France) \nTitle: K-theory and characteristic classes in topology and complex geometry  (a tribute to Atiyah and Hirzebruch)\n\n\n\n\n\nApril 20\, 2021 | 9:00 – 10:30am ET\n\n\n\n\nDan Freed (the University of Texas at Austin) \nTitle: The Atiyah-Singer Index Theorem\n\n\n\n\n\nApril 27\, 2021 | 9:00 – 10:30am ET\n\n\n\n\nFrances Kirwan (University of Oxford) \nTitle: Moment maps and the Yang-Mills functional
URL:https://cmsa.fas.harvard.edu/event/memorial-conference-for-the-founders-of-index-theory-atiyah-bott-hirzebruch-and-singer/
LOCATION:Virtual
CATEGORIES:Event,Math Science Literature Lecture Series,Special Lectures
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DTSTART;TZID=America/New_York:20210416T130000
DTEND;TZID=America/New_York:20210416T143000
DTSTAMP:20260702T104754
CREATED:20230707T171834Z
LAST-MODIFIED:20250328T201348Z
UID:10000910-1618578000-1618583400@cmsa.fas.harvard.edu
SUMMARY:CMSA Math-Science Literature Lecture: Deep Networks from First Principles
DESCRIPTION:Yi MaPhoto Copyright Noah Berger / 2019\n  \nYi Ma (University of California\, Berkeley) \nTitle: Deep Networks from First Principles \nAbstract: In this talk\, we offer an entirely “white box’’ interpretation of deep (convolution) networks from the perspective of data compression (and group invariance). In particular\, we show how modern deep layered architectures\, linear (convolution) operators and nonlinear activations\, and even all parameters can be derived from the principle of maximizing rate reduction (with group invariance). All layers\, operators\, and parameters of the network are explicitly constructed via forward propagation\, instead of learned via back propagation. All components of so-obtained network\, called ReduNet\, have precise optimization\, geometric\, and statistical interpretation. There are also several nice surprises from this principled approach: it reveals a fundamental tradeoff between invariance and sparsity for class separability; it reveals a fundamental connection between deep networks and Fourier transform for group invariance – the computational advantage in the spectral domain (why spiking neurons?); this approach also clarifies the mathematical role of forward propagation (optimization) and backward propagation (variation). In particular\, the so-obtained ReduNet is amenable to fine-tuning via both forward and backward (stochastic) propagation\, both for optimizing the same objective. This is joint work with students Yaodong Yu\, Ryan Chan\, Haozhi Qi of Berkeley\, Dr. Chong You now at Google Research\, and Professor John Wright of Columbia University. \nTalk chair: Harry Shum \nSlides | Video
URL:https://cmsa.fas.harvard.edu/event/cmsa-math-science-literature-lecture_ma/
LOCATION:Virtual
CATEGORIES:Event,Math Science Literature Lecture Series,Public Lecture,Special Lectures
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/Lecture_Ma-1-pdf.jpeg
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