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UID:440@lincs.fr
DTSTART;TZID=Europe/Paris:20190313T140000
DTEND;TZID=Europe/Paris:20190313T150000
DTSTAMP:20190319T120421Z
URL:https://www.lincs.fr/events/talk-by-sergey-dovgal/
SUMMARY:Multiparametric Boltzmann sampling and applications
DESCRIPTION:\nI will describe the problem of multiparametric generation
 (which is #P-complete) and a relaxation of this problem (multiparametric
 Boltzmann sampling) for which we construct a fully polynomial algorithm.
 Our algorithm relies on the convex optimisation techniques\, namely on the
 Exponential Conic Solver (ECOS) constructed in 2013 by Domahidi\, Chu and
 Boyd.The implementation of our algorithm is available on github. As a
 practical benchmark\, we perform the tuning of regular grammars with over
 1k tuning parameters\, 19k states and 357k transitions in about 2 hours
 (the previously known algorithm was exponential in the number of
 parameters).In order to give impression of the numerous applications of
 multiparametric sampling\, I will present six different examples:I.
 Software testing and code generation using lambda calculus\;II. Non-uniform
 sparse random graphs\;III. Belief propagation in tree decomposition for RNA
 design (Ponty\, Will\, Hammer)\;IV. Bose-Einstein condensate in quantum
 harmonic oscillator represented by integer partitions\;V. Multiclass
 queuing networks (Buši? \, Bouillard\, Rovetta)\;VI. Combinatorial
 Learning problems and connection with Maximum Likelihood approach.The
 implementation of our algorithm can be found
 at https://github.com/maciej-bendkowski/boltzmann-brain\n
CATEGORIES:Seminars,Youtube
LOCATION:Paris-Rennes Room (EIT Digital)\, 23 avenue d'Italie\, 75013
 Paris\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=23 avenue d'Italie\, 75013
 Paris\, France;X-APPLE-RADIUS=100;X-TITLE=Paris-Rennes Room (EIT
 Digital):geo:0,0
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TZID:Europe/Paris
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DTSTART:20181028T020000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
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