Математические методы прогнозирования (практика, В.В. Стрижов)/Группа 574, осень 2019
Материал из MachineLearning.
(Различия между версиями)
Строка 6: | Строка 6: | ||
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]]) ==== | |
** Generative models | ** Generative models | ||
** Applications | ** Applications | ||
** Autoregressive models (CharRNN, MADE, WaveNet, PixelCNN) | ** Autoregressive models (CharRNN, MADE, WaveNet, PixelCNN) | ||
+ | |||
+ | ==== Seminar 2 (Isachenko, [[Медиа:Isachenko2019DeepGenerativeModels2.pdf|slides]]) ==== | ||
+ | ** Generative vs discriminative | ||
+ | ** Latent variable models | ||
+ | ** Variational Inference | ||
** ELBO | ** ELBO | ||
- | * | + | ** Variational Autoencoder |
- | * | + | |
- | + | ||
- | + | ||
* Seminar 3 (Isachenko) | * Seminar 3 (Isachenko) | ||
** Inference methods of approximation | ** Inference methods of approximation |
Версия 09:34, 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