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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 - 8 из 8

Только метаданные
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.
Только метаданные
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.
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
Spiking neural network with local plasticity and sparse connectivity for audio classification
(2024) Rybka, R. B.; Vlasov, D. S.; Manzhurov, A. I.; Sboev, A. G.; Рыбка, Роман Борисович; Сбоев, Александр Георгиевич
Purpose. Studying the possibility of implementing a data classification method based on a spiking neural network, which has a low number of connections and is trained based on local plasticity rules, such as Spike-Timing-Dependent Plasticity. Methods. As the basic architecture of a spiking neural network we use a network included an input layer and layers of excitatory and inhibitory spiking neurons (Leaky Integrate and Fire). Various options for organizing connections in the selected neural network are explored. We have proposed a method for organizing connectivity between layers of neurons, in which synaptic connections are formed with a certain probability, calculated on the basis of the spatial arrangement of neurons in the layers. In this case, a limited area of connectivity leads to a higher sparseness of connections in the overall network. We use frequency-based coding of data into spike trains, and logistic regression is used for decoding. Results. As a result, based on the proposed method of organizing connections, a set of spiking neural network architectures with different connectivity coefficients for different layers of the original network was implemented. A study of the resulting spiking network architectures was carried out using the Free Spoken Digits dataset, consisting of 3000 audio recordings corresponding to 10 classes of digits from 0 to 9. Conclusion. It is shown that the proposed method of organizing connections for the selected spiking neural network allows reducing the number of connections by up to 60% compared to a fully connected architecture. At the same time, the accuracy of solving the classification problem does not deteriorate and is 0.92...0.95 according to the F1 metric. This matches the accuracy of standard support vector machine, k-nearest neighbor, and random forest classifiers. The source code for this article is publicly available: https://github.com/sag111/Sparse-WTA-SNN.
Только метаданные
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
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
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
Только метаданные
Deep Neural Networks Ensemble with Word Vector Representation Models to Resolve Coreference Resolution in Russian
(2020) Gryaznov, A.; Sboev, A.; Rybka, R.; Сбоев, Александр Георгиевич; Рыбка, Роман Борисович
© 2020, Springer Nature Switzerland AG.In this paper we present a novel neural networks ensemble to solve the task of coreference resolution in Russian texts. The ensemble consists of several neural networks, each based on recurrent Bidirectional long short-term memory layers (BiLSTM), attention mechanism, consistent scoring with selection of probable mentions and antecedents. The applied neural network topology has already shown state-of-the-art results in English for this task, and is now adapted for the Russian language. The resulting coreference markup is obtained by aggregating output scores from several blocks of independently trained neural network models. To represent an input source text, a combination of word vectors from two language models is used. We study the dependence of the coreference detection accuracy on various combinations of models of vector representation of words along with two tokenization approaches: gold markup or UDpipe tools. Finally, to show the improvement made by our ensemble approach, we present the results of experiments with both RuCor and AnCor datasets.