Works matching AU BURNAEV, E.
Results: 24
Properties of the Bayesian Parameter Estimation of a Regression Based on Gaussian Processes.
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- Journal of Mathematical Sciences, 2014, v. 203, n. 6, p. 789, doi. 10.1007/s10958-014-2168-5
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Manifold Modeling in Machine Learning.
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- Journal of Communications Technology & Electronics, 2021, v. 66, n. 6, p. 754, doi. 10.1134/S106422692106005X
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Functional Dimension Reduction in Predictive Modeling.
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- Journal of Communications Technology & Electronics, 2021, v. 66, n. 6, p. 745, doi. 10.1134/S1064226921060048
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Time-Series Classification for Industrial Applications: Road Surface Damage Detection Use Case.
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- Journal of Communications Technology & Electronics, 2020, v. 65, n. 12, p. 1491, doi. 10.1134/S1064226920120049
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Next Best View Planning via Reinforcement Learning for Scanning of Arbitrary 3D Shapes.
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- Journal of Communications Technology & Electronics, 2020, v. 65, n. 12, p. 1484, doi. 10.1134/S1064226920120141
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Algorithmic Foundations of Predictive Analytics in Industrial Engineering Design.
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- Journal of Communications Technology & Electronics, 2019, v. 64, n. 12, p. 1485, doi. 10.1134/S1064226919120039
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On Construction of Early Warning Systems for Predictive Maintenance in Aerospace Industry.
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- Journal of Communications Technology & Electronics, 2019, v. 64, n. 12, p. 1473, doi. 10.1134/S1064226919120027
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- Article
ON ASYMPTOTIC OPTIMALITY OF THE SECOND ORDER IN THE MINIMAX QUICKEST DETECTION PROBLEM OF DRIFT CHANGE FOR BROWNIAN MOTION.
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- Theory of Probability & Its Applications, 2009, v. 53, n. 3, p. 519, doi. 10.1137/S0040585X97983791
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DISORDER PROBLEM FOR POISSON PROCESS IN GENERALIZED BAYESIAN SETTING.
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- Theory of Probability & Its Applications, 2009, v. 53, n. 3, p. 500, doi. 10.1137/S0040585X9798378x
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Computationally efficient algorithm for Gaussian Process regression in case of structured samples.
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- Computational Mathematics & Mathematical Physics, 2016, v. 56, n. 4, p. 499, doi. 10.1134/S0965542516040163
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On a method for constructing ensembles of regression models.
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- Automation & Remote Control, 2013, v. 74, n. 10, p. 1630, doi. 10.1134/S0005117913100044
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Properties of the posterior distribution of a regression model based on Gaussian random fields.
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- Automation & Remote Control, 2013, v. 74, n. 10, p. 1645, doi. 10.1134/S0005117913100056
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Deforming Implicit Neural Representation Generative Adversarial Network for Unsupervised Appearence Editing.
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- Doklady Mathematics, 2024, v. 110, p. S299, doi. 10.1134/S1064562424602373
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Rethinking Graph Classification Problem in Presence of Isomorphism.
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- Doklady Mathematics, 2024, v. 110, p. S312, doi. 10.1134/S1064562424602385
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No Two Users Are Alike: Generating Audiences with Neural Clustering for Temporal Point Processes.
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- Doklady Mathematics, 2023, v. 108, p. S511, doi. 10.1134/S1064562423701661
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Barcodes as Summary of Loss Function Topology.
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- Doklady Mathematics, 2023, v. 108, p. S333, doi. 10.1134/S1064562423701570
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1-Dimensional Topological Invariants to Estimate Loss Surface Non-Convexity.
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- Doklady Mathematics, 2023, v. 108, p. S325, doi. 10.1134/S1064562423701569
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Fundamental Research and Developments in the Field of Applied Artificial Intelligence.
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- Doklady Mathematics, 2022, v. 106, p. S14, doi. 10.1134/S1064562422060047
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Fast multispectral deep fusion networks.
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- Bulletin of the Polish Academy of Sciences: Technical Sciences, 2018, v. 66, n. 6, p. 875, doi. 10.24425/bpas.2018.125935
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A model of the functional state of participants of laboratory markets.
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- Journal of Computer & Systems Sciences International, 2009, v. 48, n. 6, p. 1002, doi. 10.1134/S1064230709060148
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The influence of parameter initialization on the training time and accuracy of a nonlinear regression model.
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- Journal of Communications Technology & Electronics, 2016, v. 61, n. 6, p. 646, doi. 10.1134/S106422691606005X
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Regression on the basis of nonstationary Gaussian processes with Bayesian regularization.
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- Journal of Communications Technology & Electronics, 2016, v. 61, n. 6, p. 661, doi. 10.1134/S1064226916060061
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Surrogate modeling of multifidelity data for large samples.
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- Journal of Communications Technology & Electronics, 2015, v. 60, n. 12, p. 1348, doi. 10.1134/S1064226915120037
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On an iterative algorithm for calculating weighted principal components.
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- Journal of Communications Technology & Electronics, 2015, v. 60, n. 6, p. 619, doi. 10.1134/S1064226915060042
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