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Akademický článek

Human knowledge models: Learning applied knowledge from the data

Autor: Egor Dudyrev, Ilia Semenkov, Sergei O. Kuznetsov, Gleb Gusev, Andrew Sharp, Oleg S. Pianykh

Publikováno v: PLoS ONE, Vol 17, Iss 10 (2022)

Artificial intelligence and machine learning have demonstrated remarkable results in science and applied work. However, present AI models, developed to be run on computers but used in human-driven applications, create a visible disconnect between AI

Externí odkaz: https://doaj.org/article/062e35df2a8143c2a3ec50960905aa2c

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Predicting the Audience Size of a Tweet

Autor: Andrey Kupavskii, Alexey Umnov, Gleb Gusev, Pavel Serdyukov

Publikováno v: Proceedings of the International AAAI Conference on Web and Social Media. 7:693-696

We study information dissemination in Twitter. We present an analysis of two important characteristics of so called retweet cascades: retweet count and show count, i.e., number of users that receive the tweet in their feed. We show that these two mea

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3f6ea7055f6258c5e94791c7bf863f18
https://doi.org/10.1609/icwsm.v7i1.14454

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Impact of External Conditions on Selecting Special Transport Vehicle for Perishable Cargo Transportation

Autor: Sergei Naumenko, Gleb Gusev, Tatiana Nabatchikova, Fedor Polivoda

Publikováno v: Transportation Research Procedia. 54:445-454

The list of special transport vehicles used for transporting the perishable goods by rail in Russia is presented in the study. The maximum allowable cargo delivery time was estimated depending on the type of used vehicle. The impact of external facto

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f9e3876e31cc602b06fd7e381bbca786
https://doi.org/10.1016/j.trpro.2021.02.094

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Uncertainty Estimation of Transformer Predictions for Misclassification Detection

Autor: Artem Vazhentsev, Gleb Kuzmin, Artem Shelmanov, Akim Tsvigun, Evgenii Tsymbalov, Kirill Fedyanin, Maxim Panov, Alexander Panchenko, Gleb Gusev, Mikhail Burtsev, Manvel Avetisian, Leonid Zhukov

Publikováno v: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8e0233161569458c8d06460043ecef82
https://doi.org/10.18653/v1/2022.acl-long.566

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Adversarial Attacks on Deep Models for Financial Transaction Records

Autor: Alexey Zaytsev, Gleb Gusev, Ivan Fursov, Evgeny Burnaev, Matvey Morozov, Nina Kaploukhaya, Rodrigo Rivera-Castro, Elizaveta Kovtun, Ivan Kireev, Dmitry Babaev

Publikováno v: KDD

Machine learning models using transaction records as inputs are popular among financial institutions. The most efficient models use deep-learning architectures similar to those in the NLP community, posing a challenge due to their tremendous number o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b8990acb26b23cd2d550e5c92ad601eb
https://doi.org/10.1145/3447548.3467145

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Heat pumps for subway stations heat supply

Autor: Boris Minaev, Sergei Naumenko, Gleb Gusev, Nikolai Kostin

Publikováno v: Energy Safety and Energy Economy. 1:23-26

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0022b80df7dbcbc8a9b95fad4b454648
https://doi.org/10.18635/2071-2219-2019-1-23-26

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PvDeConv: Point-Voxel Deconvolution for Autoencoding CAD Construction in 3D

Autor: Gleb Gusev, Kseniya Cherenkova, Djamila Aouada

We propose a Point-Voxel DeConvolution (PVDeConv) module for 3D data autoencoder. To demonstrate its efficiency we learn to synthesize high-resolution point clouds of 10k points that densely describe the underlying geometry of Computer Aided Design (

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65435636cdaabbe8ed2b264696164cd9

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3D Sparse Deformation Signature for Dynamic Face Recognition

Autor: Abd El Rahman Shabayek, Kseniya Cherenkova, Gleb Gusev, Djamila Aouada

Publikováno v: ICIP

This paper proposes a novel compact and memory efficient Sparse 3D Deformation Signature (S3DS) to represent a sparse 3D deformation signal for 3D Dynamic Face Recognition. S3DS is based on a non-linear 6D-space representation that secures physically

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::76746db6119803320bca8ef2491ae14a
https://doi.org/10.1109/icip40778.2020.9190656

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3d Deformation Signature for Dynamic Face Recognition

Autor: Djamila Aouada, Abd El Rahman Shabayek, Gleb Gusev, Kseniya Cherenkova, Bjorn Ottersten

Publikováno v: ICASSP

This work proposes a novel 3D Deformation Signature (3DS) to represent a 3D deformation signal for 3D Dynamic Face Recognition. 3DS is computed given a non-linear 6D-space representation which guarantees physically plausible 3D deformations. A unique

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d2d8e319ad7b5be375fb7cfd3b20ee90
https://doi.org/10.1109/icassp40776.2020.9054733

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