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

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Short link [http://bit.ly/IS_B2 bit.ly/IS_B2]
Short link [http://bit.ly/IS_B2 bit.ly/IS_B2]
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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.
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Videolectures are available [https://www.youtube.com/playlist?list=PLk4h7dmY2eYH9RtoKGzxHKji0GLiBzSlZ here].
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==== Seminar 1 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels1.pdf‎|slides]]) ====
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* Generative models
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* Applications
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* Autoregressive models (CharRNN, MADE, WaveNet, PixelCNN)
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==== Seminar 2 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels2.pdf‎|slides]]) ====
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* Generative vs discriminative
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* Latent variable models
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* Variational Inference
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* ELBO
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* Variational Autoencoder
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==== Seminar 3 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels3.pdf‎|slides]]) ====
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* Mean field approximation
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* Flow models (NICE, RealNVP)
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==== Seminar 4 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels4.pdf‎|slides]]) ====
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* VAE Limitations
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* Flows in VAE
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* Autoregressive flows (MAF, IAF, Parallel WaveNet)
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==== Seminar 5 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels5.pdf‎|slides]]) ====
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* IWAE (lower bound, posterior, inactive units)
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* ELBO surgery
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* VampPrior
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==== Seminar 6 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels6.pdf‎|slides]]) ====
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* Autoregressive decoder in VAE
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* Posterior collapse, decoder weakening
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* Disentangled representations
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* beta-VAE
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== Группа ==
== Группа ==
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! Тест 3
! Тест 3
! Тест 4
! Тест 4
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! Тест 5
! HW 1
! HW 1
! HW 2
! HW 2
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|[[Media:Grabovoy2019Course5HM1.pdf‎|Essay]]
|[[Media:Grabovoy2019Course5HM1.pdf‎|Essay]]
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| Кислинский Вадим Геннадьевич
| Кислинский Вадим Геннадьевич
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|[https://drive.google.com/a/phystech.edu/file/d/1kkqHZDR2ATzWtf8FRc8BjbcNAXaApvy1/view?usp=drivesdk Essay]
|[https://drive.google.com/a/phystech.edu/file/d/1kkqHZDR2ATzWtf8FRc8BjbcNAXaApvy1/view?usp=drivesdk Essay]
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||[https://drive.google.com/file/d/1UkmIDJgZEDTtzSk_a20IZlTy62F2tnJB/view?usp=sharing|task_1]
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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.
 
-
 
-
==== Seminar 1 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels1.pdf‎|slides]]) ====
 
-
* Generative models
 
-
* Applications
 
-
* Autoregressive models (CharRNN, MADE, WaveNet, PixelCNN)
 
-
 
-
==== Seminar 2 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels2.pdf‎|slides]]) ====
 
-
* Generative vs discriminative
 
-
* Latent variable models
 
-
* Variational Inference
 
-
* ELBO
 
-
* Variational Autoencoder
 
-
 
-
==== Seminar 3 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels3.pdf‎|slides]]) ====
 
-
* Mean field approximation
 
-
* Flow models (NICE, RealNVP)
 
-
 
-
==== Seminar 4 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels4.pdf‎|slides]]) ====
 
-
* VAE Limitations
 
-
* Flows in VAE
 
-
* Autoregressive flows (MAF, IAF, Parallel WaveNet)
 
-
 
-
==== Seminar 5 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels5.pdf‎|slides]]) ====
 
-
* IWAE (lower bound, posterior, inactive units)
 
-
* ELBO surgery
 
-
* VampPrior
 
-
 
-
==== Seminar 6 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels6.pdf‎|slides]]) ====
 
-
* Autoregressive decoder in VAE
 
-
* Posterior collapse, decoder weakening
 
-
* Disentangled representations
 
-
* beta-VAE
 

Версия 10:29, 12 октября 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


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 -
Фельдман Даниил -
Никитин Филипп -
Фалахов И -
Собраков -


  • 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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