dlcp2025:review
Differences
This shows you the differences between two versions of the page.
Both sides previous revisionPrevious revisionNext revision | Previous revision | ||
dlcp2025:review [10/09/2025 06:45] – [Section 2. Machine Learning for Environmental Sciences] admin | dlcp2025:review [15/09/2025 21:07] (current) – [Отозваны] admin | ||
---|---|---|---|
Line 28: | Line 28: | ||
||39. ML-Based Optimum Sub-system Size for the GPU Implementation of the Tridiagonal Partition Method \\ +M. Veneva | ||39. ML-Based Optimum Sub-system Size for the GPU Implementation of the Tridiagonal Partition Method \\ +M. Veneva | ||
|| 44. Natural Image Classification via Quasi-Cyclic Graph Ensembles and Random-Bond Ising Models with Enhanced Nishimori Temperature \\ +V.S. Usatyuk | 25.08.2025 Получена \\ 26.08.2025 Исправление+ \\ 28.08.2025 Рецензирование || | || 44. Natural Image Classification via Quasi-Cyclic Graph Ensembles and Random-Bond Ising Models with Enhanced Nishimori Temperature \\ +V.S. Usatyuk | 25.08.2025 Получена \\ 26.08.2025 Исправление+ \\ 28.08.2025 Рецензирование || | ||
- | || 43. Analysis of the TAIGA-HiSCORE Data using the Latent Space of Autoencoders \\ +Yu. Dubenskaya | + | || 43. Analysis of the TAIGA-HiSCORE Data using the Latent Space of Autoencoders \\ +Yu. Dubenskaya |
|| 81. Data augmentation problem for imaging atmospheric Cherenkov telescopes in stereo mode: the TAIGA-IACT Example \\ +D. Zhurov | || 81. Data augmentation problem for imaging atmospheric Cherenkov telescopes in stereo mode: the TAIGA-IACT Example \\ +D. Zhurov | ||
- | || 95. Гамма-астрономия ультравысоких энергий и проект TAIGA-100 \\ +L.Kuzmichev | ||
- | 11 | + | 10 |
===== Section 2. Machine Learning for Environmental Sciences ===== | ===== Section 2. Machine Learning for Environmental Sciences ===== | ||
Line 46: | Line 45: | ||
|| 73. Modeling turbulent transport of passive scalars in the planetary boundary layer using large eddy simulation and machine learning \\ I.A.Gerasimov | 28.08.2025 Получена \\ 03.09.2025 Исправление2 \\ 04.09.2025 Рецензирование || | || 73. Modeling turbulent transport of passive scalars in the planetary boundary layer using large eddy simulation and machine learning \\ I.A.Gerasimov | 28.08.2025 Получена \\ 03.09.2025 Исправление2 \\ 04.09.2025 Рецензирование || | ||
|| 45. Application of Convolutional Neural Networks for Upper Ionosphere Remote Sensing Using All-Sky Camera Data. \\ A.V. Vorobev| 16.08.2025 Получена+ \\ 28.08.2025 Исправление+ \\ 18.08.2025 Рецензирование || | || 45. Application of Convolutional Neural Networks for Upper Ionosphere Remote Sensing Using All-Sky Camera Data. \\ A.V. Vorobev| 16.08.2025 Получена+ \\ 28.08.2025 Исправление+ \\ 18.08.2025 Рецензирование || | ||
- | || 82. COMPARISON OF MACHINE LEARNING METHODS FOR ACCOUNTING LAGGED RELATIONSHIPS IN URBAN HEAT ISLAND MODELING \\ K.F. Nazmutdinov | + | || 82. COMPARISON OF MACHINE LEARNING METHODS FOR ACCOUNTING LAGGED RELATIONSHIPS IN URBAN HEAT ISLAND MODELING \\ K.F. Nazmutdinov |
|| 56. Detection of Irminger Rings in high resolution ocean hydrodynamic modeling data using artificial neural networks \\ M. Kalinin | 28.08.2025 Получена \\ 03.09.2025 Исправление2 \\ 06.09.2025 Рецензирование || | || 56. Detection of Irminger Rings in high resolution ocean hydrodynamic modeling data using artificial neural networks \\ M. Kalinin | 28.08.2025 Получена \\ 03.09.2025 Исправление2 \\ 06.09.2025 Рецензирование || | ||
|| 92. Intercomparison of Machine Learning and Ingredient-Based Approaches for Identifying Hail-Prone Weather Conditions over Russia \\ P.D. Blinov | || 92. Intercomparison of Machine Learning and Ingredient-Based Approaches for Identifying Hail-Prone Weather Conditions over Russia \\ P.D. Blinov | ||
Line 84: | Line 83: | ||
|| 74. СОЗДАНИЕ ДИНАМИЧЕСКОГО КОГНОВИЗОРА – РАСПОЗНАВАНИЕ КОГНИТИВНЫХ СОСТОЯНИЙ С ПОМОЩЬЮ МЕТОДОВ ГЛУБОКОГО ОБУЧЕНИЯ \\ А.С.Макаров | 29.08.2025 Отозвана | || 74. СОЗДАНИЕ ДИНАМИЧЕСКОГО КОГНОВИЗОРА – РАСПОЗНАВАНИЕ КОГНИТИВНЫХ СОСТОЯНИЙ С ПОМОЩЬЮ МЕТОДОВ ГЛУБОКОГО ОБУЧЕНИЯ \\ А.С.Макаров | 29.08.2025 Отозвана | ||
|| 96. Machine learning for statistical downscaling of precipitation spatial distribution characteristics in the Moscow region \\ Yarinich Yulia Ivanovna | 22.08.2025 Отозвана|| | || 96. Machine learning for statistical downscaling of precipitation spatial distribution characteristics in the Moscow region \\ Yarinich Yulia Ivanovna | 22.08.2025 Отозвана|| | ||
+ | || 95. Гамма-астрономия ультравысоких энергий и проект TAIGA-100 \\ +L.Kuzmichev | ||
- | 6 | + | 7 |
++++ | ++++ |
dlcp2025/review.1757486706.txt.gz · Last modified: by admin