Математические методы прогнозирования (практика, В.В. Стрижов)/Группа 574, осень 2019
Материал из MachineLearning.
(Различия между версиями)
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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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== Группа == | == Группа == | ||
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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] | ||[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. | 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]]) ==== | + | ==== Seminar 1 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels1.pdf|slides]]) ==== |
* Generative models | * Generative models | ||
* Applications | * Applications |
Версия 10:11, 12 октября 2019
Short link bit.ly/IS_B2
Группа
5 курс
Студент | Тест 1 | Тест 2 | Тест 3 | Тест 4 | Тест 5 | HW 1 | HW 2 |
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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 |
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Сайранов Данил | - | |
Фельдман Даниил | - | |
Никитин Филипп | - | |
Фалахов И | - | |
Собраков | - |
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, 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 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