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--- ml4gamma:biblio [22/02/2025 15:56] – [Общие вопросы МО] admin
+++ ml4gamma:biblio [21/11/2025 09:29] (current) – [Общие вопросы МО] admin
@@ Line 3: / Line 3: @@
 ===== Общие вопросы МО =====
-  * [[|]]
+  * [[https://education.yandex.ru/handbook/ml|Учебник по машинному обучению Yandex]]
   * [[https://www.youtube.com/@razinkov|Разников]].
     * Оченть интересный видеокурс по МО
   * [[https://arxiv.org/abs/2404.19756v2|KAN: Kolmogorov–Arnold Networks]], arXiv:2404.19756v2
   * [[https://habr.com/ru/companies/ods/articles/442002/|Знакомство с Neural ODE]]
+  * [[https://logic.pdmi.ras.ru/~sergey/teaching/maderl20/|Лекции по МО Сергея Николенко]]
+  * [[https://habr.com/ru/articles/821547/|Метрики]] в МО
+  * [[https://habr.com/ru/companies/mws/articles/770202/|Трансформеры]] простыми словами
+===== Обратимые НС =====
+  * {{ :ml4gamma:817_analyzing_inverse_problems_wit-1-iclr-2019.pdf |Analyzing Inverse Problems with
+Invertible Neural Networks}}
+  * {{ :ml4gamma:inn_final.pdf |Invertible Neural Networks and their Applications}}
+  * {{ :ml4gamma:inns-cern-october-2020.pdf |Solving Inverse Problems with Invertible Neural Networks}}
 ===== Редкие, аномальные события =====
@@ Line 47: / Line 57: @@
   * Marco Seeland ID*, Patrick Mäder. Multi-view classification with convolutional neural networks. [[https://doi.org/10.1371/journal.pone.0245230|doi:10.1371/journal.pone.0245230]]
-===== Другие вопросы МО =====
-  * M. Drozdova et. al. Radio-astronomical Image Reconstruction with Conditional Denoising Diffusion Model, [[https://arxiv.org/pdf/2402.10204v2|arXiv:2402.10204v2]]
 ===== Гамма астрономия =====
+Очень красивая презентация [[https://gammalearn.pages.in2p3.fr/talks/20250128-ml-cosmic-workshop/|GammaLearn - Deep Learning for the CTAO event reconstruction]]
   * Ti-Pei Li and  YuQian Ma, Analysis methods for results in gamma-ray astronomy, [[https://www.researchgate.net/publication/234438870|ResearchGate]], in The Astrophysical Journal, August 1983
-  * Tilman Plehna, Anja Buttera, Barry Dillona, and Claudius Krausea// Modern Machine Learning for LHC Physicists. [[https://arxiv.org/pdf/2211.01421v1|Axiv:2211.01421v1]]
+  * Tilman Plehna, Anja Buttera, Barry Dillona, and Claudius Krausea. Modern Machine Learning for LHC Physicists. [[https://arxiv.org/pdf/2211.01421v1|ArXiv: 2211.01421v1]]
   * Hannes Warnhofer, Samuel T. Spencer, and Alison M.W. Mitchell.Multi-View Deep Learning for Imaging Atmospheric Cherenkov Telescopes. [[https://arxiv.org/pdf/2403.18516v1|arXiv:2403.18516v1]]
+==== Предварительная обработка данных ====
+  * {{ :ml4gamma:pymap_python-based_data_analysis_package_with_a_ne.pdf |Mani KhuranaKunal Kumar et. al.}}, PyMAP: Python-Based Data Analysis Package with a New Image Cleaning Method to Enhance the Sensitivity of MACE Telescope
+=== Возможная параметризация данных HiSCORE: комбинирование временных, амплитудных и пространственных меток ===
+  * Оригинальная статья: [[https://inspirehep.net/files/60350fa47ef79864e199320fa3726391 | La Parola, V., et al. "Machine Learning-Enhanced Discrimination of Gamma-Ray and Hadron Events Using Temporal Features: An ASTRI Mini-Array Analysis." Appl. Sci 15 (2025): 3879.]]
+    * {{ :ml4gamma:timingparameters_v2.pdf |Краткая выжимка (А.Д.) из статьи La Parola, V., et al.}}
+===== Другие вопросы МО =====
+  * M. Drozdova et. al. Radio-astronomical Image Reconstruction with Conditional Denoising Diffusion Model, [[https://arxiv.org/pdf/2402.10204v2|arXiv:2402.10204v2]]