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pro vyhledávání: '"KRYLOV, Vladimir"'
Autor:
Krylov, Vladimir A.
Publikováno v:
Известия Саратовского университета. Новая серия. Серия: История. Международные отношения, Vol 23, Iss 3, Pp 357-363 (2023)
Based on the materials of the memoirs of the participants of the events and the business correspondence of the embassy with the British government, the image of the Spanish people in the representations of the British ambassador to Spain – Samuel H
Externí odkaz:
https://doaj.org/article/b11c53e64c4949d38658a733c106a83c
Autor:
Krylov, Vladimir A.
Publikováno v:
Известия Саратовского университета. Новая серия. Серия: История. Международные отношения, Vol 22, Iss 3, Pp 337-343 (2022)
Based onthematerials ofthememoirs ofthe participants ofthe events andthe business correspondence ofthe embassy withthe British government, the activities of the British ambassador to Spain, Samuel Hoare, who held the post from 1940 to 1944, are consi
Externí odkaz:
https://doaj.org/article/43c8331f91894f8cbae7c6cd8249ef9c
We propose a pipeline for combined multi-class object geolocation and height estimation from street level RGB imagery, which is considered as a single available input data modality. Our solution is formulated via Markov Random Field optimization with
Externí odkaz:
http://arxiv.org/abs/2305.08232
Publikováno v:
IMVIP 2018
In this paper we propose an approach to perform semantic segmentation of 3D point cloud data by importing the geographic information from a 2D GIS layer (OpenStreetMap). The proposed automatic procedure identifies meaningful units such as buildings a
Externí odkaz:
http://arxiv.org/abs/2108.06306
Autor:
Staroverov, Maxim Sergeevich1,2 (AUTHOR) staroverov1995@gmail.com, Krylov, Vladimir Victorovich1,2,3 (AUTHOR), Lukyanchikov, Victor Alexandrovich1,3,4 (AUTHOR), Orlov, Egor Andreevich5 (AUTHOR), Veselkov, Alexey Alexandrovich6 (AUTHOR), Dydykin, Sergey Segreevich7 (AUTHOR), Shatdler, Vladislav Dmitrievich1 (AUTHOR)
Publikováno v:
Asian Journal of Neurosurgery. Jun2024, Vol. 19 Issue 2, p270-276. 7p.
We show how parameter redundancy in Convolutional Neural Network (CNN) filters can be effectively reduced by pruning in spectral domain. Specifically, the representation extracted via Discrete Cosine Transform (DCT) is more conducive for pruning than
Externí odkaz:
http://arxiv.org/abs/2010.12110
Akademický článek
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Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space. We propose to learn these filters as combinations of preset spectral filters defined by the Discrete Cosine Transform (DCT). Our propo
Externí odkaz:
http://arxiv.org/abs/2001.06570
Publikováno v:
European Signal Processing Conference (EUSIPCO) 2019
Convolutional neural networks (CNNs) are very popular nowadays for image processing. CNNs allow one to learn optimal filters in a (mostly) supervised machine learning context. However this typically requires abundant labelled training data to estimat
Externí odkaz:
http://arxiv.org/abs/1905.00135
Autor:
Grin, Andrey, Lvov, Ivan, Talypov, Aleksandr, Kordonskiy, Anton, Khushnazarov, Ulugbek, Krylov, Vladimir
Publikováno v:
In Neurocirugía March-April 2023 34(2):80-86