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

Samsung-HSE Laboratory

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

Samsung-HSE Laboratory is a research lab of the Faculty of Computer Science. The main direction of the Laboratory’s research is the construction of scalable probabilistic models. The core of the new Laboratory is a team of researchers of the Centre of Deep Learning and Bayesian Methods, with a broad expertise in the field of machine learning and Bayesian methods.

Samsung, which is one of the world's technological leaders, creates a network of joint laboratories around the world. The participation of HSE’s staff in this global project will allow them to focus on fundamental research and contact with the world's strongest research groups in the field of machine learning and artificial intelligence.

The major areas of research are:

  • Sparsification and acceleration of deep neural networks
  • Ensembles of ML algorithms
  • Uncertainty estimation and defences against adversarial attacks
  • Loss-based learning for Deep Structured Prediction
  • Stochastic optimization methods
  • Learning and inference methods for probabilistic models using tensor decomposition