Математические методы прогнозирования (практика, В.В. Стрижов)/Группа 574, осень 2019
Материал из MachineLearning.
(Различия между версиями)
(→Группа) |
(→Seminar 12 (Bakhteev, slides)) |
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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. | 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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* beta-VAE | * beta-VAE | ||
| + | ==== Seminar 7 (Bakhteev, [https://github.com/bahleg/tex_slides/raw/master/oct_19/slides1_elbo.pdf slides]) ==== | ||
| + | * Model selection statement | ||
| + | * ELBO for model selection | ||
| + | * Early Stopping is Nonparametric Variational Inference | ||
| + | * Langevin dynamics | ||
| + | ==== Seminar 8 (Bakhteev, [https://github.com/bahleg/tex_slides/raw/master/oct_19/slides2_hyper.pdf slides]) ==== | ||
| + | * Hyperparameter optimization | ||
| + | * Bi-level optimization | ||
| + | * RMD | ||
| + | * Gradient optimization | ||
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| + | ==== Seminar 9 (Grabovoy, [https://github.com/andriygav/EMprior/blob/master/Lecture/Grabovoy2019EMprior.pdf slides]) ==== | ||
| + | * Mixture of Models | ||
| + | * Mixture of Experts | ||
| + | * Priors on the local Models | ||
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| + | ==== Seminar 10 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels7.pdf|slides]]) ==== | ||
| + | * Reversible Residual Networks | ||
| + | * Glow | ||
| + | * Neural ODE | ||
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| + | ==== Seminar 11 (Bakhteev, [https://github.com/bahleg/tex_slides/raw/master/oct_19/slides3_meta.pdf slides]) ==== | ||
| + | * Meta-optimization | ||
| + | * Pruning | ||
| + | * Structure sampling | ||
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| + | ==== Seminar 12 (Bakhteev, [https://github.com/bahleg/tex_slides/raw/master/oct_19/slides4_struct.pdf slides]) ==== | ||
| + | * ARD | ||
| + | * AdaNet | ||
| + | * NAS | ||
| + | * Gumbel-Softmax | ||
| + | * Variational inference with structure generation | ||
== Группа == | == Группа == | ||
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| Сайранов Данил | | Сайранов Данил | ||
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| + | | Александра Гальцева | ||
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| - | * | + | * Topics |
** Generative adversarial networks | ** Generative adversarial networks | ||
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** Methods of model selection | ** Methods of model selection | ||
** Generalization theorem | ** Generalization theorem | ||
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** Complexity theorems | ** Complexity theorems | ||
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** 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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** Theorem of number of experts | ** Theorem of number of experts | ||
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** Prior propagation for deep learning networks | ** Prior propagation for deep learning networks | ||
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** Directional Bayesian statistics | ** Directional Bayesian statistics | ||
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** Bayesian structure learning | ** Bayesian structure learning | ||
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** Probabilistic metric space construction | ** Probabilistic metric space construction | ||
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** Informative prior | ** Informative prior | ||
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** Bayesian programming | ** Bayesian programming | ||
| - | + | ** Informative prior with applications | |
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| - | * Informative prior with applications | + | |
Текущая версия
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
Seminar 12 (Bakhteev, slides)
- ARD
- AdaNet
- NAS
- Gumbel-Softmax
- Variational inference with structure generation
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
