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Роман Рыбка, Roman Rybka
Роман Рыбка, Roman Rybka
NRC "Kurchatov Institute"
Verified email at nrcki.ru - Homepage
Title
Cited by
Cited by
Year
Self-adaptive STDP-based learning of a spiking neuron with nanocomposite memristive weights
AV Emelyanov, KE Nikiruy, AV Serenko, AV Sitnikov, MY Presnyakov, ...
Nanotechnology 31 (4), 045201, 2019
972019
Machine learning models of text categorization by author gender using topic-independent features
A Sboev, T Litvinova, D Gudovskikh, R Rybka, I Moloshnikov
Procedia Computer Science 101, 135-142, 2016
772016
Automatic gender identification of author of Russian text by machine learning and neural net algorithms in case of gender deception
A Sboev, I Moloshnikov, D Gudovskikh, A Selivanov, R Rybka, T Litvinova
Procedia computer science 123, 417-423, 2018
362018
Analytical properties of the perturbed FitzHugh–Nagumo model
NA Kudryashov, RB Rybka, AG Sboev
Applied Mathematics Letters 76, 142-147, 2018
332018
Deep learning neural nets versus traditional machine learning in gender identification of authors of rusprofiling texts
A Sboev, I Moloshnikov, D Gudovskikh, A Selivanov, R Rybka, T Litvinova
Procedia computer science 123, 424-431, 2018
272018
Solving a classification task by spiking neural network with STDP based on rate and temporal input encoding
A Sboev, A Serenko, R Rybka, D Vlasov
Mathematical Methods in the Applied Sciences 43 (13), 7802-7814, 2020
242020
Современные подходы к моделированию активности культур нейронов in vitro
КВ Анохин, МС Бурцев, ВА Ильин, ИИ Киселев, КА Кукин, КВ Лахман, ...
Математическая биология и биоинформатика 7 (2), 372-397, 2012
242012
Deep learning network models to categorize texts according to author's gender and to identify text sentiment
A Sboev, T Litvinova, I Voronina, D Gudovskikh, R Rybka
2016 International Conference on Computational Science and Computational …, 2016
202016
Solving a classification task by spiking neurons with STDP and temporal coding
A Sboev, D Vlasov, R Rybka, A Serenko
Procedia computer science 123, 494-500, 2018
192018
A quantitative method of text emotiveness evaluation on base of the psycholinguistic markers founded on morphological features
A Sboev, D Gudovskikh, R Rybka, I Moloshnikov
Procedia Computer Science 66, 307-316, 2015
182015
Morpho-syntactic parsing based on neural networks and corpus data
R Rybka, A Sboev, I Moloshnikov, D Gudovskikh
2015 Artificial Intelligence and Natural Language and Information Extraction …, 2015
152015
Modeling the dynamics of spiking networks with memristor-based STDP to solve classification tasks
A Sboev, D Vlasov, R Rybka, Y Davydov, A Serenko, V Demin
Mathematics 9 (24), 3237, 2021
142021
An algorithm of finding thematically similar documents with creating context-semantic graph based on probabilistic-entropy approach
IA Moloshnikov, AG Sboev, RB Rybka, DV Gydovskikh
Procedia Computer Science 66, 297-306, 2015
142015
Gender Prediction for Authors of Russian Texts Using Regression And Classification Techniques.
T Litvinova, P Seredin, O Litvinova, O Zagorovskaya, A Sboev, ...
CDUD@ CLA, 44-53, 2016
122016
Spiking neural network reinforcement learning method based on temporal coding and STDP
A Sboev, D Vlasov, R Rybka, A Serenko
Procedia computer science 145, 458-463, 2018
102018
Analysis of the full-size russian corpus of internet drug reviews with complex ner labeling using deep learning neural networks and language models
A Sboev, S Sboeva, I Moloshnikov, A Gryaznov, R Rybka, A Naumov, ...
Applied Sciences 12 (1), 491, 2022
92022
An analysis of full-size Russian complexly NER labelled corpus of Internet user reviews on the drugs based on deep learning and language neural nets
A Sboev, S Sboeva, I Moloshnikov, A Gryaznov, R Rybka, A Naumov, ...
arXiv preprint arXiv:2105.00059, 2021
92021
Data-driven model for emotion detection in Russian texts
A Sboev, A Naumov, R Rybka
Procedia Computer Science 190, 637-642, 2021
92021
Research of a deep learning neural network effectiveness for a morphological parser of Russian language
AG Sboev, DV Gudovskikh, IA Moloshnikov, RB Rybka, I Ivanov, ...
Komp'juternaja Lingvistika i Intellektual'nye Tehnologii, 234-244, 2017
92017
Анализ эмотивности текстов на основе психолингвистических маркеров с определением морфологических свойств
ДВ Гудовских, ИА Молошников, РБ Рыбка
Вестник Воронежского государственного университета. Серия: Лингвистика и …, 2015
92015
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