Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Anton Muravev"'
Publikováno v:
IEEE Access, Vol 9, Pp 15304-15319 (2021)
The influence of deep learning is continuously expanding across different domains, and its new applications are ubiquitous. The question of neural network design thus increases in importance, as traditional empirical approaches are reaching their lim
Externí odkaz:
https://doaj.org/article/2dea1bf6abae45b9a2e085e077c0bd08
Autor:
Anton Muravev, Ayrat Yakupov, Tatiana Gerasimova, Daut Islamov, Vladimir Lazarenko, Alexander Shokurov, Alexander Ovsyannikov, Pavel Dorovatovskii, Yan Zubavichus, Alexander Naumkin, Sofiya Selektor, Svetlana Solovieva, Igor Antipin
Publikováno v:
International Journal of Molecular Sciences; Volume 23; Issue 4; Pages: 2341
Sulfur-containing groups preorganized on macrocyclic scaffolds are well suited for liquid-phase complexation of soft metal ions; however, their binding potential was not extensively studied at the air–water interface, and the effect of thioether to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f58d5f521b3e4cf7d5bc128b930360a9
https://pubmed.ncbi.nlm.nih.gov/35216456
https://pubmed.ncbi.nlm.nih.gov/35216456
Autor:
Homayun Afrabandpey, Moncef Gabbouj, Anton Muravev, Francesco Cricri, Emre Aksu, Honglei Zhang, Hamed R. Tavakoli
Publikováno v:
2021 IEEE International Conference on Image Processing (ICIP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::987aeaf8ffc8263ae4fe76fb90a233c3
https://doi.org/10.1109/icip42928.2021.9506102
https://doi.org/10.1109/icip42928.2021.9506102
Publikováno v:
EUVIP
Deep learning algorithms (in particular Convolutional Neural Networks, or CNNs) have shown their superiority in computer vision tasks and continue to push the state of the art in the most difficult problems of the field. However, deep models freque
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1c0dc6b4f10b6441f14cbf068b1d5064
https://trepo.tuni.fi/handle/10024/129427
https://trepo.tuni.fi/handle/10024/129427
Publikováno v:
Muravev, A, Thanh Tran, D, Iosifidis, A, Kiranyaz, S & Gabbouj, M 2018, Acceleration approaches for big data analysis . in 2018 25th IEEE International Conference on Image Processing (ICIP) : Proceedings . IEEE, pp. 311-315, IEEE International Conference on Image Processing 2018, Athens, Greece, 07/10/2018 . https://doi.org/10.1109/ICIP.2018.8451082
ICIP
ICIP
The massive size of data that needs to be processed by Machine Learning models nowadays sets new challenges related to their computational complexity and memory footprint. These challenges span all processing steps involved in the application of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c8a0203f7c68e53451cf0a21dc3d1ff0
https://trepo.tuni.fi/handle/10024/129391
https://trepo.tuni.fi/handle/10024/129391
Publikováno v:
Muravev, A, Can Ozan, E, Iosifidis, A & Gabbouj, M 2017, ' Pyramid Encoding for Fast Additive Quantization ', Proceedings of the European Signal Processing Conference, vol. 2017, pp. 2575-2579 . https://doi.org/10.23919/EUSIPCO.2017.8081662
EUSIPCO
EUSIPCO
The problem of approximate nearest neighbor (ANN) search in Big Data has been tackled with a variety of recent methods. Vector quantization based solutions have been maintaining the dominant position, as they operate in the original data space, bette
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1465d6495694fbe0e81b097b280b7b14