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

Centre of Deep Learning and Bayesian Methods

Publications
Book
User-controllable Multi-texture Synthesis with Generative Adversarial Networks

Kochurov M., Volkhonskiy D., Yashkov D. et al.

Vol. 4. Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2020.

Article
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
In press

Ashukha A., Lyzhov A., Molchanov D. et al.

The Eighth International Conference on Learning Representations (Virtual Only). 2020. P. 1-9.

Book chapter
User-controllable Multi-texture Synthesis with Generative Adversarial Networks

Alanov A., Kochurov M., Volkhonskiy D. et al.

In bk.: Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2020). 2020. P. 214-221.

Working paper
Low-variance Gradient Estimates for the Plackett-Luce Distribution

Gadetsky A., Struminsky K., Robinson C. et al.

Bayesian Deep Learning NeurIPS 2019 Workshop. 2019. Bayesian Deep Learning NeurIPS 2019 Workshop, 2019

About the Center

International Laboratory of Deep Learning and Bayesian Methods is established on the basis of Bayesian Methods Research Group. The group is one of the strongest scientific groups in Russia in the area of machine learning and probabilistic modeling. The laboratory researches the neurobayesian models that combine the advantages of the two most successful machine learning approaches, namely neural networks and Bayesian methods.