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

Материал из MachineLearning.

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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)

Seminar 2 (Isachenko, slides)

    • Generative vs discriminative
    • Latent variable models
    • Variational Inference
    • ELBO
    • Variational Autoencoder
  • 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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