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Рыбка, Роман Борисович

Организационные подразделения

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Институт лазерных и плазменных технологий

Стратегическая цель Института ЛаПлаз – стать ведущей научной школой и ядром развития инноваций по лазерным, плазменным, радиационным и ускорительным технологиям, с уникальными образовательными программами, востребованными на российском и мировом рынке образовательных услуг.

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Роман Борисович

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Теперь показываю 1 - 10 из 14

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Neural-network method for determining text author's sentiment to an aspect specified by the named entity
(2020) Naumov, A.; Rybka, R.; Sboev, A.; Selivanov, A.; Gryaznov, A.; Рыбка, Роман Борисович; Сбоев, Александр Георгиевич
© 2020 Copyright for this paper by its authors.This study presents the approach to aspect-based sentiment analysis where a named entity of a certain category is considered as an aspect. Such task formulation is a novelty and opens up the opportunity to determine writers' attitudes to organizations and people considered in texts. This task required a dataset of Russian-language sentences where sentiment with respect to certain named entities would be labeled, which we collected using a crowdsourcing platform. Sentiment determination is based on a deep neural network with attention mechanism and ELMo language model for word vector representation. The proposed model is validated on available data on a similar task. The resulting performance (by the f1-micro metric) on the collected dataset is 0.72, which is the new state of the art for the Russian language.
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Comparison of Bagging and Sparcity Methods for Connectivity Reduction in Spiking Neural Networks with Memristive Plasticity
(2024) Rybka, R.; Davydov, Y.; Vlasov, D.; Sboev, A.; Рыбка, Роман Борисович; Сбоев, Александр Георгиевич
Developing a spiking neural network architecture that could prospectively be trained on energy-efficient neuromorphic hardware to solve various data analysis tasks requires satisfying the limitations of prospective analog or digital hardware, i.e., local learning and limited numbers of connections, respectively. In this work, we compare two methods of connectivity reduction that are applicable to spiking networks with local plasticity; instead of a large fully-connected network (which is used as the baseline for comparison), we employ either an ensemble of independent small networks or a network with probabilistic sparse connectivity. We evaluate both of these methods with a three-layer spiking neural network, which are applied to handwritten and spoken digit classification tasks using two memristive plasticity models and the classical spike time-dependent plasticity (STDP) rule. Both methods achieve an F1-score of 0.93ў??0.95 on the handwritten digits recognition task and 0.85ў??0.93 on the spoken digits recognition task. Applying a combination of both methods made it possible to obtain highly accurate models while reducing the number of connections by more than three times compared to the basic model.
Только метаданные
Ensemble of spiking neural networks with STDP and temporal encoding for classification problems
(2025) Vlasov, D.; Sboev, A.; Rybka, R.; Davydov, Y. u.; Сбоев, Александр Георгиевич; Рыбка, Роман Борисович
Только метаданные
Analysis of neural network methods for obtaining soliton solutions of the nonlinear Schrodinger equation
(2025) Moloshnikov, I. A.; Sboev, A. G.; Kutukov, A. A.; Rybka, R. B.; Zavertyaev, S. V.; Молошников, Иван Александрович; Сбоев, Александр Георгиевич; Кутуков, Александр Алексеевич; Рыбка, Роман Борисович; Завертяев, Савелий Васильевич
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Image and Audio Data Classification Using Bagging Ensembles of Spiking Neural Networks with Memristive Plasticity
(2024) Rybka, R. B.; Davydov, Y.; Sboev, A.; Vlasov, D.; Рыбка, Роман Борисович; Сбоев, Александр Георгиевич
Только метаданные
Towards Solving Classification Tasks Using Spiking Neurons with Fixed Weights
(2023) Sboev, A. G.; Serenko, A. V.; Kunitsyn, D. E.; Rybka, R. B.; Сбоев, Александр Георгиевич; Рыбка, Роман Борисович
Только метаданные
Combining aggregated attention and transformer architecture for accurate and efficient performance of Spiking Neural Networks
(2025) Zhang, H.; Sboev, A.; Rybka, R.; Yu, Q.; Сбоев, Александр Георгиевич; Рыбка, Роман Борисович
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Direct Correlational Spike-Timing-Dependent Plasticity Learning Applied to Classification Tasks
(2025) Sboev, A.; Kunitsyn, D.; Davydov, Y.; Rybka, R.; Рыбка, Роман Борисович
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Ensembling SNNs with STDP Learning on Base of Rate Stabilization for Image Classification
(2021) Serenko, A.; Sboev, A.; Rybka, R.; Vlasov, D.; Сбоев, Александр Георгиевич; Рыбка, Роман Борисович
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.In spite of a number of existing spiking neural network models for image classification, it still remains relevant from both methodological and practical points of view to develop a model as simple as possible, while at the same time applicable to classification tasks with various types of data, be it real vectors or images. Our previous work proposed a simple spiking network with Spike-Timing-Dependent-Plasticity (STDP) learning for solving real-vector classification tasks. In this paper, that method is extended to image recognition tasks and enhanced by aggregating neurons into ensembles. The network comprises one layer of neurons with STDP-plastic inputs receiving pixels of input images encoded with spiking rates. This work considers two approaches for aggregating neurons’ output activities within an ensemble: by averaging their output spiking rates (i.e. averaging outputs before decoding spiking rates into class labels) and by voting with decoded class labels. Ensembles aggregated by output frequencies are shown to achieve a significant accuracy increase up to 95% (by F1-score) for the Optdigits handwritten digit dataset, and is comparable with conventional machine learning approaches.
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Application of Machine Learning to Construct Solitons of Generalized Nonlinear Schrodinger Equation
(2024) Sboev, A. G.; Kudryashov, N. A.; Moloshnikov, I. A.; Nifontov, D. R.; Rybka, R. B.; Сбоев, Александр Георгиевич; Кудряшов, Николай Алексеевич; Молошников, Иван Александрович; Нифонтов, Даниил Романович; Рыбка, Роман Борисович