EDUCATION

• M.A., Applied Mathematics and Physics, Moscow Institute of Physics and Technology
• Ph.D., Theoretical Computer Science, Dorodnitsyn Computing Center of Russian Academy of Sciences

•  Thesis at PhysTech: “Some estimations of generalization capabilities of learning algorithms”

•  Ph.D. dissertation: “Predicate description of supplementary constraints in pattern recognition problems”

Zhumekenov, A., Takhanov, R., Castro, A.J., Assylbekov, Z.
Approximation error of Fourier neural networks
(2021) Statistical Analysis and Data Mining, 14 (3), pp. 258-270. 

Tezekbayev, M., Assylbekov, Z., Takhanov, R.
Semantics-and syntax-related subvectors in the skip-gram embeddings (student abstract)
(2020) AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, pp. 13937-13938. 

Assylbekov, Z., Takhanov, R.
Context vectors are reflections of word vectors in half the dimensions
(2020) IJCAI International Joint Conference on Artificial Intelligence, 2021-January, pp. 5115-5119. 

Uteuliyeva, M., Zhumekenov, A., Takhanov, R., Assylbekov, Z., Castro, A.J., Kabdolov, O.
Fourier neural networks: A comparative study
(2020) Intelligent Data Analysis, 24 (5), pp. 1107-1120. 

Assylbekov, Z., Takhanov, R.
Context vectors are reflections of word vectors in half the dimensions
(2019) Journal of Artificial Intelligence Research, 66, pp. 225-242. 

Bekembayev, A., Takhanov, R., Ten, V., Shymyr, M.
Neural nets use for satellite telemetry analysis in application for Kazstsat mission
(2019) Proceedings of the International Astronautical Congress, IAC, 2019-October, art. no. IAC-19_B4_6A_10_x54983, . 

Assylbekov, Z., Takhanov, R.
Reusing weights in subword-Aware neural language models
(2018) NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 1, pp. 1413-1423. 

Takhanov, R.
Hybrid VCSPs with crisp and valued conservative templates
(2017) Leibniz International Proceedings in Informatics, LIPIcs, 92, . 

Assylbekov, Z., Takhanov, R., Myrzakhmetov, B., Washington, J.N.
Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones
(2017) EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings, pp. 1866-1872. 

Takhanov, R., Assylbekov, Z.
Patterns Versus Characters in Subword-Aware Neural Language Modeling
(2017) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10635 LNCS, pp. 157-166. 

Kolmogorov, V., Takhanov, R.
Inference Algorithms for Pattern-Based CRFs on Sequence Data
(2016) Algorithmica, 76 (1), pp. 17-46. 

Kolmogorov, V., Rolínek, M., Takhanov, R.
Effectiveness of structural restrictions for hybrid CSPs
(2015) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9472, pp. 566-577. 

Takhanov, R., Kolmogorov, V.
Inference algorithms for pattern-based CRFs on sequence data
(2013) 30th International Conference on Machine Learning, ICML 2013, (PART 2), pp. 1182-1190. 

Takhanov, R.
A dichotomy theorem for the general minimum cost homomorphism problem
(2010) Leibniz International Proceedings in Informatics, LIPIcs, 5, pp. 657-668. 

Takhanov, R.S.
On the sample monotonization problem
(2010) Computational Mathematics and Mathematical Physics, 50 (7), pp. 1260-1266. 

Takhanov, R.
Extensions of the minimum cost homomorphism problem
(2010) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6196 LNCS, pp. 328-337. 

Takhanov, R.S.
Maximum predicate descriptions of sets of mappings
(2007) Computational Mathematics and Mathematical Physics, 47 (9), pp. 1570-1581. 

Takhanov, R.S.
Predicate description of universal constraints in the algebraic approach to pattern recognition problems
(2007) Computational Mathematics and Mathematical Physics, 47 (3), pp. 527-532.

Stochastic Processes, Statistical Learning