Zobrazeno 1 - 10
of 1 180
pro vyhledávání: '"P. A. Rogov"'
Autor:
Dontsov, Alexey, Korzh, Dmitrii, Zhavoronkin, Alexey, Mikheev, Boris, Bobkov, Denis, Alanov, Aibek, Rogov, Oleg Y., Oseledets, Ivan, Tutubalina, Elena
Machine Unlearning (MU) is critical for enhancing privacy and security in deep learning models, particularly in large multimodal language models (MLLMs), by removing specific private or hazardous information. While MU has made significant progress in
Externí odkaz:
http://arxiv.org/abs/2410.18057
Autor:
Borodin, Kirill, Kudryavtsev, Vasiliy, Korzh, Dmitrii, Efimenko, Alexey, Mkrtchian, Grach, Gorodnichev, Mikhail, Rogov, Oleg Y.
Automatic Speaker Verification (ASV) systems, which identify speakers based on their voice characteristics, have numerous applications, such as user authentication in financial transactions, exclusive access control in smart devices, and forensic fra
Externí odkaz:
http://arxiv.org/abs/2408.17352
Publikováno v:
Омский научный вестник: Серия "Авиационно-ракетное и энергетическое машиностроение", Vol 3, Iss 4, Pp 43-48 (2019)
The article discusses approaches for calculation of a piston compressor unit with a direct gas piston drive for the compression of natural gas during long-term production in a depleted gas formation. This task is relevant for gas companies from the
Externí odkaz:
https://doaj.org/article/1adadc9f733f4c278bf898972439f7df
While Deep Neural Networks (DNNs) have demonstrated remarkable performance in tasks related to perception and control, there are still several unresolved concerns regarding the privacy of their training data, particularly in the context of vulnerabil
Externí odkaz:
http://arxiv.org/abs/2405.07562
Speaker recognition technology is applied in various tasks ranging from personal virtual assistants to secure access systems. However, the robustness of these systems against adversarial attacks, particularly to additive perturbations, remains a sign
Externí odkaz:
http://arxiv.org/abs/2404.18791
Publikováno v:
Chaos, Solitons & Fractals, 115197, Volume 186, 2024
Modulation instability is a phenomenon of spontaneous pattern formation in nonlinear media, oftentimes leading to an unpredictable behaviour and a degradation of a signal of interest. We propose an approach based on reinforcement learning to suppress
Externí odkaz:
http://arxiv.org/abs/2404.04310
As deep learning (DL) models are widely and effectively used in Machine Learning as a Service (MLaaS) platforms, there is a rapidly growing interest in DL watermarking techniques that can be used to confirm the ownership of a particular model. Unfort
Externí odkaz:
http://arxiv.org/abs/2401.08261
The characterization of interactions between autophagy modifiers (Atg8-family proteins) and their natural ligands (peptides and proteins) or small molecules is important for a detailed understanding of selective autophagy mechanisms and for design of
Externí odkaz:
http://arxiv.org/abs/2401.04453
Autor:
Afanasiev, S., Agakishiev, G., Aleksandrov, E., Aleksandrov, I., Alekseev, P., Alishina, K., Astakhov, V., Atkin, E., Aushev, T., Azorskiy, V., Babkin, V., Balashov, N., Barak, R., Baranov, A., Baranov, D., Baranova, N., Barbashina, N., Baznat, M., Bazylev, S., Belov, M., Blau, D., Bocharnikov, V., Bogdanova, G., Bolozdynya, A., Bondar, E., Boos, E., Buryakov, M., Buzin, S., Chebotov, A., Chemezov, D., Chen, J. H., Demanov, A., Dementev, D., Dmitriev, A., Drnoyan, J., Dryablov, D., Dryuk, A., Dubinchik, B., Dulov, P., Egorov, A., Egorov, D., Elsha, V., Fediunin, A., Fedosimova, A., Filippov, I., Filozova, I., Finogeev, D., Gabdrakhmanov, I., Galavanov, A., Gavrischuk, O., Gertsenberger, K., Golosov, O., Golovatyuk, V., Grigoriev, P., Golubeva, M., Guber, F., Ibraimova, S., Idrisov, D., Idrissova, T., Iusupova, A., Ivashkin, A., Izvestnyy, A., Kabadzhov, V., Kanokova, Sh., Kapishin, M., Kapitonov, I., Karjavin, V., Karmanov, D., Karpushkin, N., Kattabekov, R., Kekelidze, V., Khabarov, S., Kharlamov, P., Khudaiberdyev, G., Khukhaeva, A., Khvorostukhin, A., Kiryushin, Yu., Klimai, P., Kolesnikov, V., Kolozhvari, A., Kopylov, Yu., Korolev, M., Kovachev, L., Kovalev, I., Kruglova, I., Kovalev, Yu., Kozlov, I., Kozlov, V., Kuklin, S., Kulish, E., Kurganov, A., Kutergina, V., Kuznetsov, A., Ladygin, E., Lanskoy, D., Lashmanov, N., Lebedev, I., Lenivenko, V., Lednicky, R., Leontiev, V., Liapin, D., Litvinenko, E., Ma, Y. G., Makankin, A., Makhnev, A., Malakhov, A., Mamaev, M., Martemianov, A., Martovitsky, E., Mashitsin, K., Merkin, M., Merts, S., Morozov, S., Murin, Yu., Musaev, K., Musulmanbekov, G., Myasnikov, A., Myktybekov, D., Nagdasev, R., Nemnyugin, S., Nikitin, D., Novozhilov, S., Olimov, Kh., Olimov, K., Palichik, V., Parfenov, P., Pelevanyuk, I., Peresunko, D., Piyadin, S., Platonova, M., Plotnikov, V., Podgainy, D., Pukhaeva, N., Ratnikov, F., Reshetova, S., Rogov, V., Romanov, I., Rufanov, I., Rukoyatkin, P., Rumyantsev, M., Rybakov, T., Sakulin, D., Sedykh, S., Serebryakov, D., Shabanov, A., Segal, I., Semak, A., Sergeev, S., Serikkanov, A., Sheremetev, A., Sheremeteva, A., Shchipunov, A., Shitenkov, M., Shopova, M., Shumikhin, V., Shutov, A., Shutov, V., Shodmonov, M., Slepnev, I., Slepnev, V., Slepov, I., Smirnov, A., Smolyanin, T., Solomin, A., Sorin, A., Sosnovtsev, V., Spaskov, V., Stavinskiy, A., Stekhanov, V., Stepanenko, Yu., Streletskaya, E., Streltsova, O., Strikhanov, M., Sukhov, E., Suvarieva, D., Taer, G., Taranenko, A., Tarasov, N., Tarasov, O., Teremkov, P., Terletsky, A., Teryaev, O., Tcholakov, V., Tikhomirov, V., Timoshenko, A., Tojiboev, O., Topilin, N., Tretyakova, T., Troshin, V., Truttse, A., Tserruya, I., Tskhay, V., Tyapkin, I., Ustinov, V., Vasendina, V., Velichkov, V., Volkov, V., Voronin, A., Voytishin, N., Yuldashev, B., Yurevich, V., Zamiatin, N., Zavertyaev, M., Zhang, S., Zhavoronkova, I., Zhezher, V., Zhigareva, N., Zinchenko, A., Zubankov, A., Zubarev, E., Zuev, M.
BM@N (Baryonic Matter at Nuclotron) is the first experiment operating and taking data at the Nuclotron/NICA ion-accelerating complex.The aim of the BM@N experiment is to study interactions of relativistic heavy-ion beams with fixed targets. We presen
Externí odkaz:
http://arxiv.org/abs/2312.17573
Autor:
Getahun, Melaku N., Rogov, Oleg Y., Dylov, Dmitry V., Somov, Andrey, Bouridane, Ahmed, Hamoudi, Rifat
Retinal vascular segmentation, is a widely researched subject in biomedical image processing, aims to relieve ophthalmologists' workload when treating and detecting retinal disorders. However, segmenting retinal vessels has its own set of challenges,
Externí odkaz:
http://arxiv.org/abs/2311.08059