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Statistical Physics of Learning and Inference

Biehl, M., Caticha, N., Opper, M. & Villmann, T., Apr-2019, Proc. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning : ESANN 2019 . Verleysen, M. (ed.). Ciaco - i6doc.com, 9 p.

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The exchange of ideas between statistical physics
and computer science has been very fruitful and is currently gaining momentum as a consequence of the revived interest in neural networks, machine learning
and inference in general.
Statistical physics methods complement other approaches to the theoretical understanding of machine learning processes and inference in stochastic modeling. They facilitate, for instance, the study of dynamical and equilibrium properties of randomized training processes in model situations.
At the same time, the approach inspires novel and efficient algorithms and facilitates interdisciplinary applications in a variety of scientific and technical disciplines.
Original languageEnglish
Title of host publicationProc. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Subtitle of host publicationESANN 2019
EditorsMichel Verleysen
PublisherCiaco - i6doc.com
Number of pages9
Publication statusPublished - Apr-2019
EventSpecial Session at ESANN 2019: Statistical Physics of Learning and Inference - Brugge, Belgium
Duration: 24-Apr-201926-Apr-2019
https://www.elen.ucl.ac.be/esann/index.php?pg=specsess#statistical

Conference

ConferenceSpecial Session at ESANN 2019
CountryBelgium
CityBrugge
Period24/04/201926/04/2019
Internet address

Event

Special Session at ESANN 2019: Statistical Physics of Learning and Inference

24/04/201926/04/2019

Brugge, Belgium

Event: Conference

Related Activities
  1. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

    Michiel Straat (Speaker), Michael Biehl (Participant)
    24-Apr-201926-Apr-2019

    Activity: Participating in or organising an eventParticipation in conferenceAcademic

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