• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Maksim Kuznetsov
Tensoral Methods in Generative Models Consisting Discrete Stochastic Nodes
Statistical Learning Theory
(Master’s programme)
2019
Some real world data, such as images from several distinct classes, can be modeled with discrete latent variables. However, training discrete latent variable models remains a challenge and all methods suffer from either high bias or high variance of the gradient estimate compared to training continuous models. In this Master's thesis, we propose a family of unbiased methods for training Variational Autoencoders with discrete latent variables that are based on a structured decomposition of the log-likelihood tensor. The proposed family generalizes a known algorithm —— MuProp —— to more expressive decompositions of the loss tensor and shows state of the art results in terms of Evidence Lower Bound convergence on a set of vision tasks.

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses