Scalable gaussian processes with billions of inducing inputs via tensor train decomposition P Izmailov, A Novikov, D Kropotov International Conference on Artificial Intelligence and Statistics, 726-735 |
A superlinearly-convergent proximal Newton-type method for the optimization of finite sums A Rodomanov, D Kropotov International Conference on Machine Learning, 2597-2605 |
Massive MIMO adaptive modulation and coding using online deep learning algorithm E Bobrov, D Kropotov, H Lu, D Zaev IEEE Communications Letters 26 (4), 818-822 |
A randomized coordinate descent method with volume sampling A Rodomanov, D Kropotov SIAM Journal on Optimization 30 (3), 1878-1904 |
On one method of non-diagonal regularization in sparse Bayesian learning D Kropotov, D Vetrov Proceedings of the 24th international conference on Machine learning, 457-464 |
Automatic Determination of the Number of Components in the EM Algorithm of Restoration of a Mixture of Normal Distributions DP Vetrov, DA Kropotov, AA Osokin Computational Mathematics and Mathematical Physics 50, 733-746 |
Knowledge representation and acquisition in expert systems for pattern recognition OM Vasil’ev, DP Vetrov, DA Kropotov Computational mathematics and mathematical physics 47, 1373-1397 |
3-D mouse brain model reconstruction from a sequence of 2-D slices in application to Allen brain atlas A Osokin, D Vetrov, D Kropotov Computational Intelligence Methods for Bioinformatics and Biostatistics: 6th … |
The Methods of Dependencies Description with the Help of Optimal Multistage Partitioning OV Senko, AV Kuznetsova, DA Kropotov Proceedings of the 18th International Workshop on Statistical Modelling … |
Mars: Masked automatic ranks selection in tensor decompositions M Kodryan, D Kropotov, D Vetrov International Conference on Artificial Intelligence and Statistics, 3718-3732 |
Variational segmentation algorithms with label frequency constraints D Kropotov, D Laptev, A Osokin, D Vetrov Pattern Recognition and Image Analysis 20, 324-334 |
Variational relevance vector machine for tabular data D Kropotov, D Vetrov, L Wolf, T Hassner Proceedings of 2nd Asian Conference on Machine Learning, 79-94 |
Automatic determination of the numbers of components in the EM algorithm for the restoration of a mixture of normal distributions DP Vetrov, DA Kropotov, AA Osokin Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki 50 (4), 770-783 |
Optimal Bayesian classifier with arbitrary gaussian regularizer D Kropotov, D Vetrov Proc. of 7th Open German-Russian Workshop on Pattern Recognition and Image … |
Algoritmy vybora modelei i postroeniya kollektivnykh reshenii v zadachakh klassifikatsii, osnovannye na printsipe ustoichivosti DP Vetrov, DA Kropotov Algorithms for Model Selection and Constructing Collective Solutions in … |
The use of stability principle for kernel determination in relevance vector machines D Kropotov, D Vetrov, N Ptashko, O Vasiliev Neural Information Processing: 13th International Conference, ICONIP 2006 … |
RECOGNITION: A Universal Software System for Recognition, Data Mining, and Forecasting YI Zhuravlev, VV Ryazanov, OV Senko, AS Biryukov, DP Vetrov, ... Pattern Recognition and Image Analysis (Advances in Mathematical Theory and … |
The program system for intellectual data analysis, recognition and forecasting YI Zhuravlev, VV Ryazanov, OV Senko, AS Biryukov, DP Vetrov, ... WSEAS Transactions on Information Science and Applications 2 (1), 55-58 |
The use of bayesian framework for kernel selection in vector machines classifiers D Kropotov, N Ptashko, D Vetrov Progress in Pattern Recognition, Image Analysis and Applications: 10th … |
Machine learning methods for spectral efficiency prediction in massive mimo systems E Bobrov, S Troshin, N Chirkova, E Lobacheva, S Panchenko, D Vetrov, ... arXiv preprint arXiv:2112.14423 |
