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

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==== Seminar 1 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels1.pdf‎|slides]]) ====
==== Seminar 1 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels1.pdf‎|slides]]) ====
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** Generative models
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* Generative models
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** Applications
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* Applications
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** Autoregressive models (CharRNN, MADE, WaveNet, PixelCNN)
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* Autoregressive models (CharRNN, MADE, WaveNet, PixelCNN)
==== Seminar 2 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels2.pdf‎|slides]]) ====
==== Seminar 2 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels2.pdf‎|slides]]) ====
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** Generative vs discriminative
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* Generative vs discriminative
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** Latent variable models
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* Latent variable models
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** Variational Inference
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* Variational Inference
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** ELBO
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* ELBO
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** Variational Autoencoder
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* Variational Autoencoder
* Seminar 3 (Isachenko)
* Seminar 3 (Isachenko)

Версия 09:35, 13 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)

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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