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Regular version of the site
ФКН
Contacts

Dean — Ivan Arzhantsev

 

First Deputy Dean — Tamara Voznesenskaya

 

Deputy Dean for Research and International Relations — Sergei Obiedkov

 

Deputy Dean for Development, Finance and Administration — Irina Plisetskaya

Phone: +7 (495) 772-95-90 * 12332

computerscience@hse.ru

125319, Moscow, 3 Kochnovsky Proezd (near metro station 'Aeroport'). 

Article
First measurement of the CP-violating phase ϕdds in B s 0 → (K+π−)(K−π+) decays

Ratnikov F., Баранов А. С., Borisyak M. A. et al.

Journal of High Energy Physics. 2018. Vol. 2018. P. 1-31.

Book chapter
Efficient Mining of Subsample-Stable Graph Patterns

Buzmakov A. V., Kuznetsov S., Napoli A.

In bk.: 2017 IEEE 17th International Conference on Data Mining (ICDM). New Orleans: IEEE, 2017. Ch. 89. P. 757-762.

Book chapter
Spatially Adaptive Computation Time for Residual Networks

Figurnov M., Collins M. D., Zhu Y. et al.

In bk.: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017). Curran Associates, Inc., 2017. P. 1039-1048.

Article
Structural Instability in Single-Crystal Rare-Earth Scandium Borates RESc3(BO3)4

Kuz’micheva G. M., Kaurova I. A., Rybakov V. B. et al.

Crystal Growth & Design. 2018. Vol. 18. No. 3. P. 1571-1580.

Colloquium: Learning on networks of distributions for discrete data. Speaker: Wray Buntine, Monash University

Event ended

I will motivate the talk by reviewing some state of the art models for problems like matrix factorisation models for link prediction and tweet clustering. Then I will review the classes of distributions that can be strung together in networks to generate discrete data. This allows a rich class of models that, in its simplest form, covers things like Poisson matrix factorisation, Latent Dirichlet allocation, and Stochastic block models, but, more generally, covers complex hierarchical models on network and text data. The distributions covered include so-called non-parametric distributions such as the Gamma process. Accompanying these are a set of collapsing and augmentation techniques that are used to generate fast Gibbs samplers for many models in this class. To complete this picture, turning complex network models into fast Gibbs samplers, I will illustrate our recent methods of doing matrix factorisation with side information (e.g., GloVe word embeddings), done for link prediction, for instance, for citation networks.

Venue:
Moscow, Kochnovsky pr.,3, room 317, 18:10 

Everyone interested is welcome to attend.

If you need a pass to HSE, please contact computerscience@hse.ru