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, 2018 | 69 | 2018 |
A superlinearly-convergent proximal Newton-type method for the optimization of finite sums A Rodomanov, D Kropotov International Conference on Machine Learning, 2597-2605, 2016 | 47 | 2016 |
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, 2021 | 27 | 2021 |
A randomized coordinate descent method with volume sampling A Rodomanov, D Kropotov SIAM Journal on Optimization 30 (3), 1878-1904, 2020 | 14 | 2020 |
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, 2007 | 13 | 2007 |
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, 2010 | 8 | 2010 |
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, 2007 | 8 | 2007 |
Mars: Masked automatic ranks selection in tensor decompositions M Kodryan, D Kropotov, D Vetrov International Conference on Artificial Intelligence and Statistics, 3718-3732, 2023 | 7 | 2023 |
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 …, 2010 | 7 | 2010 |
The Methods of Dependencies Description with the Help of Optimal Multistage Partitioning OV Senko, AV Kuznetsova, DA Kropotov Proceedings of the 18-th International Workshop on Statistical Modelling …, 2003 | 7 | 2003 |
Variational segmentation algorithms with label frequency constraints D Kropotov, D Laptev, A Osokin, D Vetrov Pattern Recognition and Image Analysis 20, 324-334, 2010 | 6 | 2010 |
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, 2010 | 5 | 2010 |
Optimal Bayesian classifier with arbitrary gaussian regularizer D Kropotov, D Vetrov Proc. of 7th Open German-Russian Workshop on Pattern Recognition and Image …, 2007 | 5 | 2007 |
Algoritmy vybora modeley i postroeniya kollektivnykh resheniy v zadachakh klassifikatsii, osnovannye na printsipe ustoychivosti [Algorithms for choosing models and constructing … DP Vetrov, DA Kropotov Moscow, URSS, 2006 | 5 | 2006 |
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 …, 2006 | 5 | 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 …, 2005 | 5 | 2005 |
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, 2005 | 5 | 2005 |
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 …, 2005 | 5 | 2005 |
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, 2021 | 4 | 2021 |
Hamiltonian Monte-Carlo for orthogonal matrices V Yanush, D Kropotov arXiv preprint arXiv:1901.08045, 2019 | 4 | 2019 |