Zobrazeno 1 - 10
of 516
pro vyhledávání: '"Nikitin, A. O."'
Autor:
Nikitin, Nikolay O., Pinchuk, Maiia, Pokrovskii, Valerii, Shevchenko, Peter, Getmanov, Andrey, Aksenkin, Yaroslav, Revin, Ilia, Stebenkov, Andrey, Poslavskaya, Ekaterina, Kalyuzhnaya, Anna V.
Automated machine learning (AutoML) systems propose an end-to-end solution to a given machine learning problem, creating either fixed or flexible pipelines. Fixed pipelines are task independent constructs: their general composition remains the same,
Externí odkaz:
http://arxiv.org/abs/2312.14770
Autor:
Borisova, Julia, Nikitin, Nikolay O.
The modeling and forecasting of sea ice conditions in the Arctic region are important tasks for ship routing, offshore oil production, and environmental monitoring. We propose the adaptive surrogate modeling approach named LANE-SI (Lightweight Automa
Externí odkaz:
http://arxiv.org/abs/2312.04330
Autor:
Nikitin, Nikolay O., Teryoshkin, Sergey, Pokrovskii, Valerii, Pakulin, Sergey, Nasonov, Denis
Resource-intensive computations are a major factor that limits the effectiveness of automated machine learning solutions. In the paper, we propose a modular approach that can be used to increase the quality of evolutionary optimization for modelling
Externí odkaz:
http://arxiv.org/abs/2301.05102
Autor:
Timofeeva, Anna M.1,2 (AUTHOR) anna.m.timofeeva@gmail.com, Nikitin, Artem O.1 (AUTHOR), Nevinsky, Georgy A.1,2 (AUTHOR)
Publikováno v:
Non-Coding RNA. Oct2024, Vol. 10 Issue 5, p48. 16p.
Autor:
Starodubcev, Nikita O., Nikitin, Nikolay O., Gavaza, Konstantin G., Andronova, Elizaveta A., Sidorenko, Denis O., Kalyuzhnaya, Anna V.
In recent years generative design techniques have become firmly established in numerous applied fields, especially in engineering. These methods are demonstrating intensive growth owing to promising outlook. However, existing approaches are limited b
Externí odkaz:
http://arxiv.org/abs/2207.14621
In the paper, a multi-objective evolutionary surrogate-assisted approach for the fast and effective generative design of coastal breakwaters is proposed. To approximate the computationally expensive objective functions, the deep convolutional neural
Externí odkaz:
http://arxiv.org/abs/2204.03400
Autor:
Nikitin, Nikolay O., Pinchuk, Maiia, Pokrovskii, Valerii, Shevchenko, Peter, Getmanov, Andrey, Aksenkin, Yaroslav, Revin, Ilia, Stebenkov, Andrey, Latypov, Vladimir, Poslavskaya, Ekaterina, Kalyuzhnaya, Anna V.
Publikováno v:
In Knowledge-Based Systems 25 October 2024 302
Autor:
Hvatov, Alexander, Maslyaev, Mikhail, Polonskaya, Iana S., Sarafanov, Mikhail, Merezhnikov, Mark, Nikitin, Nikolay O.
In modern data science, it is often not enough to obtain only a data-driven model with a good prediction quality. On the contrary, it is more interesting to understand the properties of the model, which parts could be replaced to obtain better result
Externí odkaz:
http://arxiv.org/abs/2107.03146
Autor:
Valeeva, Elena V., Sabirov, Ilnur S., Safiullina, Liliya R., Nikitin, Dmitriy O., Semina, Irina I., Rees, Tim, Fesenko, Denis O., Ahmetov, Ildus I.
Publikováno v:
In Research in Autism Spectrum Disorders June 2024 114
Autor:
Nikitin, Nikolay O., Vychuzhanin, Pavel, Sarafanov, Mikhail, Polonskaia, Iana S., Revin, Ilia, Barabanova, Irina V., Maximov, Gleb, Kalyuzhnaya, Anna V., Boukhanovsky, Alexander
The effectiveness of the machine learning methods for real-world tasks depends on the proper structure of the modeling pipeline. The proposed approach is aimed to automate the design of composite machine learning pipelines, which is equivalent to com
Externí odkaz:
http://arxiv.org/abs/2106.15397