Neurobayesian models 2020

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Версия от 14:00, 20 января 2020; Alexgri (обсуждение | вклад)

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Lector: Dmitry Vetrov

Tutors: Alexander Grishin, Kirill Struminsky, Dmitry Molchanov, Kirill Neklyudov, Artem Sobolev, Arsenii Ashukha, Oleg Ivanov, Ekaterina Lobacheva, Artyom Gadetsky, Aibek Alanov.

Contacts: All the questions should be addressed to Subject line of any letter must contain the following tag: [HSE NBM20]. Letters without the tag will be most probably lost in the inbox.

We also have a chat in Telegram (invite TBA). Its main language is English. All important news will be announced in the chat.

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.

Course syllabus TBA


Grading System

The assessment consists of 4 practical assignments and a final oral exam. Practical assignments consist of programming some models/methods from the course in Python and analysing their behavior: VAE, Normalizing flows, Sparse Variational Dropout and Discrete Latent Variables. 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.

Grades for the practical assignments are TBA. If О_cumulative or О_final has a fractional part greater or equal than 0.5 then it is rounded up.


  • The course contains four practical assignments. Solutions should be submitted to anytask (url TBA). To get the invite, please write to the course e-mail. The site has an interface only in Russian, so non-Russian speaking students may submit their solutions to the course e-mail. In this case, the subject line of the letter in addition to the tag should contain your name, surname and assignment number.
  • All assignments should be coded in Python 3 using PyTorch.
  • Students have to complete all assignments by themselves. Using code of your colleagues or code from open implementations is prohibited and will be considered as plagiarism. All involved students (including those who shared their solutions) will be severely punished.
  • Assignments are scored up to TBA. Each 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. Usually you will have 2 weeks to solve an assignment. Some assignments may contain bonus parts.

Approximate dates of assignments' upload: TBA

A hard deadline for all assignments: TBA



Course Plan


Course Materials

List of materials from previous year (relevant papers, blogposts, etc.)

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

BayesGroup page