Математические методы прогнозирования (практика, В.В. Стрижов)/Группа 574, осень 2019

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This series of seminars continues the course Bayesian model selection and investigates the theoretical aspects of model selection in various application problems.
This series of seminars continues the course Bayesian model selection and investigates the theoretical aspects of model selection in various application problems.
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* Seminar 1 (Isachenko)
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* Seminar 1 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels.pdf‎|slides]])
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** Plate notation and Bayesian inference in examples
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** Generative models
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** (reminder Coherent Bayesian Inference)
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** Applications
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** Variational inference
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** Autoregressive models (CharRNN, MADE, WaveNet, PixelCNN)
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** Variational autoencoder
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** ELBO
** ELBO
* Seminar 2 (Isachenko)
* Seminar 2 (Isachenko)

Версия 15:50, 8 сентября 2019


Short link bit.ly/IS_B2

This series of seminars continues the course Bayesian model selection and investigates the theoretical aspects of model selection in various application problems.

  • Seminar 1 (Isachenko, slides)
    • Generative models
    • Applications
    • Autoregressive models (CharRNN, MADE, WaveNet, PixelCNN)
    • ELBO
  • Seminar 2 (Isachenko)
    • Analytic methods of approximation
    • Statistic sum approximation
    • Generative versus discriminative
  • Seminar 3 (Isachenko)
    • Inference methods of approximation
    • Zoo of variational autoencoders and practical examples
  • Seminar 4 (Isachenko)
    • Generative adversarial networks
  • Seminar 5 (Bakhteev)
    • Methods of model selection
    • Generalization theorem
  • Seminar 6 (Bakhteev)
    • Complexity theorems
  • Seminar 7 (Grabovoy?)
    • Mixture of experts
    • Priors on the mixture
    • Privileged learning and distilling
  • Seminar 8 (Aduenko?)
    • Theorem of number of experts
  • Seminar 9 (Vladimirova?)
    • Prior propagation for deep learning networks
  • Seminar 10
    • Directional Bayesian statistics
  • Seminar 11
    • Bayesian structure learning
  • Seminar 12
    • Probabilistic metric space construction
  • Seminar 13
    • Informative prior
  • Seminar 14
    • Bayesian programming






  • Informative prior with applications
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