Просмотр коллекции по группе - По тематике DEEP NEURAL NETWORKS
Отображение результатов 1 до 6 из 6
| Дата публикации | Название | Авторы |
| 2023 | Assessing the Mass Transfer Coefficient in Jet Bioreactors with Classical Computer Vision Methods and Neural Networks Algorithms | Nizovtseva, I.; Palmin, V.; Simkin, I.; Starodumov, I.; Mikushin, P.; Nozik, A.; Hamitov, T.; Ivanov, S.; Vikharev, S.; Zinovev, A.; Svitich, V.; Mogilev, M.; Nikishina, M.; Kraev, S.; Yurchenko, S.; Mityashin, T.; Chernushkin, D.; Kalyuzhnaya, A.; Blyakhman, F. |
| 2021 | Basic methods of malware analysis by deep neural networks | Ulyanikhin, E. I.; Ульянихин, Е. И. |
| 2019 | Comparison of UNet, ENet, and BoxENet for Segmentation of Mast Cells in Scans of Histological Slices | Karimov, A.; Razumov, A.; Manbatchurina, R.; Simonova, K.; Donets, I.; Vlasova, A.; Khramtsova, Y.; Ushenin, K. |
| 2022 | Deep Machine Learning Potentials for Multicomponent Metallic Melts: Development, Predictability and Compositional Transferability | Ryltsev, R. E.; Chtchelkatchev, N. M. |
| 2022 | Modeling the influence of the composition of refractory elements on the heat resistance of nickel alloys by a deep learning artificial neural network | Tarasov, D. A.; Milder, O. B.; Tiagunov, A. G. |
| 2019 | Survey on software tools that implement deep learning algorithms on intel/x86 and IBM/Power8/Power9 platforms | Shaikhislamov, D.; Sozykin, A.; Voevodin, V. |
