Математические методы прогнозирования (практика, В.В. Стрижов)/Группа 574, осень 2019
Материал из MachineLearning.
(Различия между версиями)
(Новая: {{Main|Численные методы обучения по прецедентам (практика, В.В. Стрижов)}} __NOTOC__ Short link [ ] This series of seminars contin...) |
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** Privileged learning and distilling | ** Privileged learning and distilling | ||
* Seminar 5 (Aduenko?) | * Seminar 5 (Aduenko?) | ||
| - | ** Theorem | + | ** Theorem of number of experts |
* Seminar 6 (Isachenko) | * Seminar 6 (Isachenko) | ||
** Generative versus discriminative | ** Generative versus discriminative | ||
Версия 21:43, 26 августа 2019
Short link [ ]
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)
- Plate notation and Bayesian inference in examples
- Variational inference
- Variational autoencoder
- ELBO
- Seminar 2 (Bakhteev)
- Methods of model selection
- Generalization theorem
- Seminar 3 (Bakhteev)
- Complexity theorems
- Seminar 4 (Grabovoy?)
- Mixture of experts
- Priors on the mixture
- Privileged learning and distilling
- Seminar 5 (Aduenko?)
- Theorem of number of experts
- Seminar 6 (Isachenko)
- Generative versus discriminative
- Seminar 7 (Isachenko)
- Zoo of variational autoencoders
- Seminar 8 (Isachenko)
- GAN
- 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
