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Mark Cerenzia, Dickson Instructor, Department of Mathematics, University of Chicago
Nov 5, 2019 - 11:25am to 12:30pm
Department of Applied Mathematics - Seminar - RE 119
Random matrix statistics emerge in a broad class of strongly correlated systems, with evidence suggesting they can play a universal role comparable to the one Gaussian and Poisson distributions do classically. Indeed, studies have identified these statistics among heavy nucleii, Riemann zeta zeros... read more
Tue.
11/5/19
Jason Morton - Department of Mathematics, Pennsylvania State University
Nov 4, 2019 - 1:50pm to 2:55pm
Department of Applied Mathematics - Colloquia - Siegal Hall, Room 118
The restricted Boltzmann machine was the original building block of deep learning models.  An implicit description of the set of probability distributions it can represent is very difficult, and is unknown even in the smallest nontrivial case.  However there are aspects of the RBM's... read more
Mon.
11/4/19
Jason Morton– Department of Mathematics, Pennsylvania State University
Nov 1, 2019 - 12:45pm to 1:45pm
Department of Applied Mathematics - Seminar - RE 114
Many statistical models for discrete data have a set of algebraic invariants that vanish on any probability distribution which is a member of the model.  We discuss a new idea for performing goodness-of-fit tests on these models using such algebraic invariants.  This approach may work... read more
Fri.
11/1/19

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