Персона:
Сбоев, Александр Георгиевич

Организационные подразделения

Организационная единица

Цель ИЯФиТ и стратегия развития - создание и развитие научно-образовательного центра мирового уровня в области ядерной физики и технологий, радиационного материаловедения, физики элементарных частиц, астрофизики и космофизики.

Фамилия

Сбоев

Имя

Александр Георгиевич

Полная страница элемента

Результаты поиска

Теперь показываю 1 - 10 из 67

Только метаданные
Memristor-based spiking neural network with online reinforcement learning
(2023) Vlasov, D.; Minnekhanov, A.; Rybka, R.; Davydov, Y.; Sboev, A. G.; Сбоев, Александр Георгиевич
Neural networks implemented in memristor-based hardware can provide fast and efficient in-memory computation, but traditional learning methods such as error back-propagation are hardly feasible in it. Spiking neural networks (SNNs) are highly promising in this regard, as their weights can be changed locally in a self-organized manner without the demand for high-precision changes calculated with the use of information almost from the entire network. This problem is rather relevant for solving control tasks with neural-network reinforcement learning methods, as those are highly sensitive to any source of stochasticity in a model initialization, training, or decision-making procedure. This paper presents an online reinforcement learning algorithm in which the change of connection weights is carried out after processing each environment state during interaction-with-environment data generation. Another novel feature of the algorithm is that it is applied to SNNs with memristor-based STDP-like learning rules. The plasticity functions are obtained from real memristors based on poly-p-xylylene and CoFeB-LiNbO
Открытый доступ
ПРОГРАММА МОДЕЛИРОВАНИЯ ОБУЧЕНИЯ СПАЙКОВОЙ СЕТИ С STDP С КОРРЕЛЯЦИОННЫМ КОДИРОВАНИЕМ ВХОДНЫХ ДАННЫХ
(Федеральное государственное бюджетное учреждение «Национальный исследовательский центр «Курчатовский институт», 2022) Серенко, А. В.; Сбоев, А. Г.; Рыбка, Р. Б.; Сбоев, Александр Георгиевич; Рыбка, Роман Борисович
Программа реализует решение типовых классификационных задач ирисов Фишера и Висконсинского рака груди путём обучения спайковой нейронной сети с синаптической пластичностью Spike-Timing-Dependent Plasticity (STDP). Входные данные кодируются взаимной корреляцией входных спайковых последовательностей. Результат классификации декодируется путём сравнения корреляции выходных последовательностей спайков со входными на тестовой выборке с распределением этих корреляций на обучающей выборке.
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.
Открытый доступ
Нейросетевое моделирование и машинное обучение на основе экспериментальных и наблюдательных данных
(НИЦ "Курчатовский институт", 2021) Сбоев, А. Г.; Сбоев, Александр Георгиевич; Кудряшов, Н. .
Только метаданные
Neural Network Modeling of Optical Solitons Described by the Generalized Nonlinear Schrodinger Equation of the Sixth Order with High Nonlinearity
(2025) Moloshnikov, I. A.; Kuvakin, M. S.; Sboev, A. G.; Сбоев, Александр Георгиевич
Только метаданные
Solving a classification task by spiking neural network with STDP based on rate and temporal input encoding
(2020) Serenko, A.; Rybka, R.; Sboev, A.; Vlasov, D.; Сбоев, Александр Георгиевич
© 2020 John Wiley & Sons, Ltd.This paper develops local learning algorithms to solve a classification task with the help of biologically inspired mathematical models of spiking neural networks involving the mechanism of spike-timing-dependent plasticity (STDP). The advantages of the models are their simplicity and, hence, the potential ability to be hardware-implemented in low-energy-consuming biomorphic computing devices. The methods developed are based on two key effects observed in neurons with STDP: mean firing rate stabilization and memorizing repeating spike patterns. As the result, two algorithms to solve a classification task with a spiking neural network are proposed: the first based on rate encoding of the input data and the second based on temporal encoding. The accuracy of the algorithms is tested on the benchmark classification tasks of Fisher's Iris and Wisconsin breast cancer, with several combinations of input data normalization and preprocessing. The respective accuracies are 99% and 94% by F1-score.
Только метаданные
On the accuracy of Covid-19 forecasting methods in Russia for two years
(2022) Moloshnikov, I. A.; Sboev, A. G.; Naumov, A. V.; Zavertyaev, S. V.; Rybka, R. B.; Сбоев, Александр Георгиевич
The effectiveness of predicting the dynamics of the coronavirus pandemic for Russia as a whole and for Moscow is studied for a two-year period beginning March 2020. The comparison includes well-proven population models and statistic methods along with a new data-driven model based on the LSTM neural network. The latter model is trained on a set of Russian regions simultaneously, and predicts the total number of cases on the 14-day forecast horizon. Prediction accuracy is estimated by the mean absolute percent error (MAPE). The results show that all the considered models, both simple and more complex, have similar efficiency. The lowest error achieved is 18% MAPE for Moscow and 8% MAPE for Russia.
Только метаданные
Actor-Critic Spiking Neural Network with RSTDP Actor Learning and TD-LTP Critic Learning
(2024) Tihomirov, Y.; Rybka, R.; Serenko, A.; Sboev, A. G.; Сбоев, Александр Георгиевич
Только метаданные
Baseline Accuracies of Forecasting COVID-19 Cases in Russian Regions on a Year in Retrospect Using Basic Statistical and Machine Learning Methods
(2021) Naumov, A. V.; Moloshnikov, I. A.; Serenko, A. V.; Rybka, R. B.; Sboev, A. G.; Сбоев, Александр Георгиевич
© 2021 The Author(s).The large amount of data accumulated so far on the dynamics of the COVID-19 outbreak has allowed assessing the accuracy of forecasting methods in retrospect. This work compares several basic time series analysis methods, including machine learning methods, for forecasting the number of confirmed cases for some days ahead. Year-long data for all regions of Russia has been used from the Yandex DataLens platform. As a result, accuracy estimates for these basic methods have been obtained for Russian regions and Russia as a whole, in dependence on the forecasting horizon. The best basic models for forecasting for 14 days are exponential smoothing and ARIMA, with an error of 11-19% by the MAPE metric for the latest part of the course of the epidemic. The accuracies obtained can be considered as baselines for more complex prospective models.
Только метаданные
A gender identification of text author in mixture of Russian multi-genre texts with distortions on base of data-driven approach using machine learning models
(2019) Gudovskikh, D.; Moloshnikov, I.; Rybka, R.; Sboev, A.; Сбоев, Александр Георгиевич
© 2019 Author(s).In this work we investigate a wide set of machine learning models of data-driven approaches (Long Short-Term Memory networks, Convolutional neural networks, multilayer perceptrons, Random Forest Classifiers, Logistic Regression and Gradient Boosting Classifiers with different sets of features) to identify the gender of author in Russian multi-genre texts in the case of existing style distortions and gender deceptions in training and testing sets. We consider and evaluate accuracy for the following situations: the influence of style distortions and gender deceptions in training texts for different genre, and the case when such deception is present only in test results. A comparison with known literature data is presented. The set of data corpora includes: one collected by a crowdsourcing platform, essays of Russian students (RusPersonality), Gender Imitation corpus, and the corpora used at Forum for Information Retrieval Evaluation 2017 (FIRE), containing texts from Facebook, Twitter and Reviews. We present the analysis of numerical experiments based on different features (morphological data, vector of character n-gram frequencies, LIWC and others) of input texts along with various machine learning models. The presented results, obtained on a wide set of data-driven models, establish the accuracy level for the task to identify gender of an author of a Russian text in the multi-genre case and analyzed the effect of the presence of deception in the test and training sets.