Neurobayesian models 2019
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Lector: Dmitry Vetrov
Tutors: Alexander Grishin, Kirill Struminsky, Dmitry Molchanov, Kirill Neklyudov, Artem Sobolev, Arsenii Ashukha, Oleg Ivanov, Ekaterina Lobacheva.
Contacts: All the questions should be addressed to bayesml@gmail.com. Theme of any letter must contain the following tag: [HSE NBM19]. Letters without the tag will be most probably lost in the inbox.
We also have a chat in Telegram (link to it was sent to the group email). It's main language is Russian, but all the questions in English will be answered in English. All important news will be announced in English in the chat and also sent to the group e-mail.
Содержание
[убрать]Course description
This course is devoted to Bayesian reasoning in application to deep learning models. Attendees would learn how to use probabilistic modeling to construct neural generative and discriminative models, how to use the paradigm of generative adversarial networks to perform approximate Bayesian inference and how to model the uncertainty about the weights of neural networks. Selected open problems in the field of deep learning would also be discussed. The practical assignments will cover implementation of several modern Bayesian deep learning models.
News
Grading System
The assessment consist of 3 practical assignments and a final oral exam. Practical assignments consist in programming some models/methods from the course in Python and analysing their behavior: VAE, Normalizing flows, Sparse Variational Dropout. At the final exam students have to demonstrate knowledge of the material covered during the entire course.
Final course grade is obtained from the following formula:
О_final = 0,7 * О_cumulative + 0,3 * О_exam,
where О_cumulative is an average grade for the practical assignments.
All grades are in ten-point grading scale. If О_cumulative or О_final has a fractional part greater or equal than 0.5 then it is rounded up.
Assignments
There are three practical assignments. Usually, they are submitted in anytask. To get the invite please write to course mail. The site has only Russian interface so the foreign student can submit to course mail. In this case, the subject line should consist of your name, surname and assignment number.
All assignments should be coded in Python 3.
Students have to complete all assignments by themselves. If the solution was discussed together, or any third-party codes and materials were used, then this should be written in the report. Otherwise, “similar” solution would be considered as plagiarism and all involved students (including those who share his solution) will be severely punished.
Assignments are scored up to 10 points. Each practical assignment has a deadline, a penalty is charged in the amount of 0.3 points for each day of delay, but in total not more than 6 points. Some assignments may contain bonus part.
Approximate dates for homework assignments (they can change!): TBA
At the end of the module before the exam there will be a hard deadline for all assignments! Exact date will be announced later.
Exam
TBA
Course Plan
| Занятие | Дата | Название | Материалы |
|---|---|---|---|
| 1 | 24 September | Lecture: Stochastic Variational Inference | article |
| 2 | 31 September | Seminar: Application of SVI to Latent Dirichlet Allocation model | article |
| Lecture: Doubly Stochastic Variational Inference | TBA | ||
| 3 | 7 October | Seminar: Doubly Stochastic Variational Inference | TBA |
| Lecture: Variational autoencoders (VAE) and normalizing flows (NF) | VAE article, NF article | ||
| 3 | 14 October | Seminar: Importance Weighted Autoencoders + more complex NF | TBA |
| Lecture: Density ratio estimation + alpha-GAN | article | ||
| 3 | 21 October | Seminar: f-GAN | article |
| Lecture: Bayesian neural networks | article, article, article | ||
| 3 | 28 October | Seminar: Local reparametrization trick | article |
| Lecture: Bayesian compression of neural networks | article, article | ||
| 3 | 7 November | Seminar: Deep Marcov chain Monte Carlo (MCMC) | article article |
| Lecture: Variance Reduction | article | ||
| 3 | 14 November | Seminar: Discrete latent variables | article article article |
| Lecture: Semi-implicit variational inference | article, article | ||
| 3 | 21 November | Seminar: VampPrior | article, article |
Reading List
- Murphy K.P. Machine Learning: A Probabilistic Perspective. The MIT Press, 2012.
- Bishop C.M. Pattern Recognition and Machine Learning. Springer, 2006.
- Mackay D.J.C. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.
- Ian Goodfellow, Yoshua Bengio & Aaron Courville. Deep Learning. MIT Press, 2016.
Useful links
[The same course in Russian at MSU] (contains more materials in Russian).
BayesGroup page.
