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PRODID:Mathematics Seminar Calendar
BEGIN:VEVENT
UID:20221205T013754-29415@math.msu.edu
DTSTAMP:20221205T013754Z
SUMMARY:Super-Resolution on the Two-Dimensional Unit Sphere
DESCRIPTION:Speaker\: Anna Veselovska, Department of Mathematics, Technical University of Munich, Germany\r\nIn this talk, we discuss the problem of recovering an atomic measure on the unit 2-sphere S^2 given finitely many moments with respect to spherical harmonics. The analysis relies on the formulation of this problem as an optimization problem on the space of bounded Borel measures on S^2 as suggested by Y. de\r\nCastro & F. Gamboa and E. Candes & C. Fernandez-Granda. We construct a dual certificate using a kernel given in an explicit form and make a concrete analysis of the interpolation problem. We support our theoretical results by various numerical examples related to direct solution of the optimization\r\nproblem and its discretization.\r\n\r\nThis is a joint work with Frank Filbir and Kristof Schroder.
LOCATION:C304 Wells Hall
DTSTART:20220916T200000Z
DTEND:20220916T210000Z
URL:https://math.msu.edu/Seminars/TalkView.aspx?talk=29415
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BEGIN:VEVENT
UID:20221205T013754-29438@math.msu.edu
DTSTAMP:20221205T013754Z
SUMMARY:Quantizing neural networks
DESCRIPTION:Speaker\: Rayan Saab, University of California, San Diego (UCSD)\r\nNeural networks are highly non-linear functions often parametrized by a staggering number of weights. Miniaturizing these networks and implementing them in hardware is a direction of research that is fueled by a practical need, and at the same time connects to interesting mathematical problems. For example, by quantizing, or replacing the weights of a neural network with quantized (e.g., binary) counterparts, massive savings in cost, computation time, memory, and power consumption can be attained. Of course, one wishes to attain these savings while preserving the action of the function on domains of interest. \r\n\r\nWe present data-driven and computationally efficient methods for quantizing the weights of already trained neural networks and we prove that our methods have favorable error guarantees under a variety of assumptions. We also discuss extensions and provide the results of numerical experiments, on large multi-layer networks, to illustrate the performance of our methods. Time permitting, we will also discuss open problems and related areas of research.
LOCATION:Online (virtual meeting)
DTSTART:20220922T183000Z
DTEND:20220922T193000Z
URL:https://math.msu.edu/Seminars/TalkView.aspx?talk=29438
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BEGIN:VEVENT
UID:20221205T013754-29435@math.msu.edu
DTSTAMP:20221205T013754Z
SUMMARY:1W-MINDS talk (passcode is the first prime number > 100).
DESCRIPTION:Speaker\: Weijie Su, University of Pennsylvania\r\nWhat Should a Good Deep Neural Network Look Like? Insights from a Layer-Peeled Model and the Law of Equi-Separation\r\n\r\n\r\nSee https://sites.google.com/view/minds-seminar/home\r\n
LOCATION:Online (virtual meeting)
DTSTART:20221006T183000Z
DTEND:20221006T193000Z
URL:https://math.msu.edu/Seminars/TalkView.aspx?talk=29435
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BEGIN:VEVENT
UID:20221205T013754-29437@math.msu.edu
DTSTAMP:20221205T013754Z
SUMMARY:1W-MINDS talk (passcode is the first prime number > 100).
DESCRIPTION:Speaker\: Dustin Mixon, Ohio State University\r\nSee https://sites.google.com/view/minds-seminar/home\r\n
LOCATION:Online (virtual meeting)
DTSTART:20221013T183000Z
DTEND:20221013T193000Z
URL:https://math.msu.edu/Seminars/TalkView.aspx?talk=29437
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BEGIN:VEVENT
UID:20221205T013754-30456@math.msu.edu
DTSTAMP:20221205T013754Z
SUMMARY:1W-MINDS talk (passcode is the first prime number > 100).
DESCRIPTION:Speaker\: Guanghui Lan, Georgia Institute of Technology\r\nSee https://sites.google.com/view/minds-seminar/home\r\n
LOCATION:Online (virtual meeting)
DTSTART:20221027T183000Z
DTEND:20221027T193000Z
URL:https://math.msu.edu/Seminars/TalkView.aspx?talk=30456
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BEGIN:VEVENT
UID:20221205T013754-30457@math.msu.edu
DTSTAMP:20221205T013754Z
SUMMARY:1W-MINDS talk (passcode is the first prime number > 100).
DESCRIPTION:Speaker\: Alex Townsend , Cornell University\r\nSee https://sites.google.com/view/minds-seminar/home\r\n
LOCATION:Online (virtual meeting)
DTSTART:20221103T183000Z
DTEND:20221103T193000Z
URL:https://math.msu.edu/Seminars/TalkView.aspx?talk=30457
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BEGIN:VEVENT
UID:20221205T013754-30458@math.msu.edu
DTSTAMP:20221205T013754Z
SUMMARY:1W-MINDS talk (passcode is the first prime number > 100).
DESCRIPTION:Speaker\: Wei Zhu , University of Massachusetts Amherst\r\nSee https://sites.google.com/view/minds-seminar/home\r\n
LOCATION:Online (virtual meeting)
DTSTART:20221117T193000Z
DTEND:20221117T203000Z
URL:https://math.msu.edu/Seminars/TalkView.aspx?talk=30458
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BEGIN:VEVENT
UID:20221205T013754-30485@math.msu.edu
DTSTAMP:20221205T013754Z
SUMMARY:1W-MINDS talk (passcode is the first prime number > 100).
DESCRIPTION:Speaker\: Zhaoran Wang, Northwestern University\r\nSee https://sites.google.com/view/minds-seminar/home\r\n
LOCATION:Online (virtual meeting)
DTSTART:20221201T193000Z
DTEND:20221201T203000Z
URL:https://math.msu.edu/Seminars/TalkView.aspx?talk=30485
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BEGIN:VEVENT
UID:20221205T013754-30459@math.msu.edu
DTSTAMP:20221205T013754Z
SUMMARY:1W-MINDS talk (passcode is the first prime number > 100).
DESCRIPTION:Speaker\: Rongjie Lai , Rensselaer Polytechnic Institute\r\nSee https://sites.google.com/view/minds-seminar/home\r\n
LOCATION:Online (virtual meeting)
DTSTART:20221208T193000Z
DTEND:20221208T203000Z
URL:https://math.msu.edu/Seminars/TalkView.aspx?talk=30459
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