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

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* beta-VAE
* beta-VAE
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==== Seminar 7 (Bakhteev, [https://github.com/bahleg/tex_slides/raw/master/oct_19/slides1_elbo.pdf slides]) ====
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* Model selection statement
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* ELBO for model selection
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* Early Stopping is Nonparametric Variational Inference
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* Langevin dynamics
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==== Seminar 8 (Bakhteev, [https://github.com/bahleg/tex_slides/raw/master/oct_19/slides2_hyper.pdf slides]) ====
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* Hyperparameter optimization
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* Bi-level optimization
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* RMD
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* Gradient optimization
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==== Seminar 9 (Grabovoy, [https://github.com/andriygav/EMprior/blob/master/Lecture/Grabovoy2019EMprior.pdf slides]) ====
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* Mixture of Models
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* Mixture of Experts
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* Priors on the local Models
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==== Seminar 10 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels7.pdf‎|slides]]) ====
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* Reversible Residual Networks
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* Glow
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* Neural ODE
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==== Seminar 11 (Bakhteev, [https://github.com/bahleg/tex_slides/raw/master/oct_19/slides3_meta.pdf slides]) ====
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* Meta-optimization
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* Pruning
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* Structure sampling
== Группа ==
== Группа ==
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| Сайранов Данил
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| Александра Гальцева
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* Seminar 4 (Isachenko)
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* Topics
** Generative adversarial networks
** Generative adversarial networks
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* Seminar 5 (Bakhteev)
 
** Methods of model selection
** Methods of model selection
** Generalization theorem
** Generalization theorem
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* Seminar 6 (Bakhteev)
 
** Complexity theorems
** Complexity theorems
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* Seminar 7 (Grabovoy?)
 
** Mixture of experts
** Mixture of experts
** Priors on the mixture
** Priors on the mixture
** Privileged learning and distilling
** Privileged learning and distilling
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* Seminar 8 (Aduenko?)
 
** Theorem of number of experts
** Theorem of number of experts
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* Seminar 9 (Vladimirova?)
 
** Prior propagation for deep learning networks
** Prior propagation for deep learning networks
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* Seminar 10
 
** Directional Bayesian statistics
** Directional Bayesian statistics
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* Seminar 11
 
** Bayesian structure learning
** Bayesian structure learning
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* Seminar 12
 
** Probabilistic metric space construction
** Probabilistic metric space construction
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* Seminar 13
 
** Informative prior
** Informative prior
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* Seminar 14
 
** Bayesian programming
** Bayesian programming
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** Informative prior with applications
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* Informative prior with applications
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Версия 20:02, 14 ноября 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.

Videolectures are available here.

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, slides)

  • Mean field approximation
  • Flow models (NICE, RealNVP)

Seminar 4 (Isachenko, slides)

  • VAE Limitations
  • Flows in VAE
  • Autoregressive flows (MAF, IAF, Parallel WaveNet)

Seminar 5 (Isachenko, slides)

  • IWAE (lower bound, posterior, inactive units)
  • ELBO surgery
  • VampPrior

Seminar 6 (Isachenko, slides)

  • Autoregressive decoder in VAE
  • Posterior collapse, decoder weakening
  • Disentangled representations
  • beta-VAE

Seminar 7 (Bakhteev, slides)

  • Model selection statement
  • ELBO for model selection
  • Early Stopping is Nonparametric Variational Inference
  • Langevin dynamics

Seminar 8 (Bakhteev, slides)

  • Hyperparameter optimization
  • Bi-level optimization
  • RMD
  • Gradient optimization

Seminar 9 (Grabovoy, slides)

  • Mixture of Models
  • Mixture of Experts
  • Priors on the local Models

Seminar 10 (Isachenko, slides)

  • Reversible Residual Networks
  • Glow
  • Neural ODE

Seminar 11 (Bakhteev, slides)

  • Meta-optimization
  • Pruning
  • Structure sampling

Группа

5 курс

Студент Тест 1 Тест 2 Тест 3 Тест 4 Тест 5 HW 1 HW 2
Васильев Илья - - 0.59 - - -
Гадаев Тамаз Тазикоевич 0.56 0.94 0.75 - 0.88 -
Гладин Егор Леонидович - - - - - -
Грабовой Андрей Валериевич 0.63 0.31 0.67 0 - Essay
Кислинский Вадим Геннадьевич - - - - - -
Козлинский Евгений Михайлович - - - - - -
Криницкий Константин Денисович - 0.25 - - - essay
Кириллов Егор Дмитриевич - - - - - -
Рогозина Анна Андреевна - - - - - -
Плетнев Никита Вячеславович 0.82 0.25 0.67 - 0.63 Essay
Малиновский Григорий Станиславович 0.82 0.81 0.84 1 0.63 [1]
Самохина Алина Максимовна - - 0.25 1 0.75 -
Султанов Азат Русланович - - - - - -
Федосов Павел Андреевич - - - - - -
Шульгин Егор Владимирович - - 0.34 - - 0.13

6 курс

Студент HW 1 HW 2
Сайранов Данил -
Александра Гальцева -
Фельдман Даниил -
Никитин Филипп -
Фалахов И -
Собраков -


  • Topics
    • Generative adversarial networks
    • Methods of model selection
    • Generalization theorem
    • Complexity theorems
    • Mixture of experts
    • Priors on the mixture
    • Privileged learning and distilling
    • Theorem of number of experts
    • Prior propagation for deep learning networks
    • Directional Bayesian statistics
    • Bayesian structure learning
    • Probabilistic metric space construction
    • Informative prior
    • Bayesian programming
    • Informative prior with applications
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