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ml4gamma:biblio [22/02/2025 15:56] – [Общие вопросы МО] adminml4gamma:biblio [20/01/2026 08:51] (current) – [Редкие, аномальные события] admin
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 ===== Общие вопросы МО ===== ===== Общие вопросы МО =====
  
-  * [[|]]+  * [[https://education.yandex.ru/handbook/ml|Учебник по машинному обучению Yandex]]
   * [[https://www.youtube.com/@razinkov|Разников]].   * [[https://www.youtube.com/@razinkov|Разников]].
     * Оченть интересный видеокурс по МО     * Оченть интересный видеокурс по МО
   * [[https://arxiv.org/abs/2404.19756v2|KAN: Kolmogorov–Arnold Networks]], arXiv:2404.19756v2   * [[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://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 ===== ===== Редкие, аномальные события =====
  
-**Общие обзоры**+==== Общие обзоры ====
  
   * Liu, Jiaqi, Guoyang Xie, Jinbao Wang, Shangnian Li, Chengjie Wang, Feng Zheng, and Yaochu Jin. [[https://link.springer.com/article/10.1007/s11633-023-1459-z|"Deep industrial image anomaly detection: A survey."]] Machine Intelligence Research 21, no. 1 (2024): 104-135.    * Liu, Jiaqi, Guoyang Xie, Jinbao Wang, Shangnian Li, Chengjie Wang, Feng Zheng, and Yaochu Jin. [[https://link.springer.com/article/10.1007/s11633-023-1459-z|"Deep industrial image anomaly detection: A survey."]] Machine Intelligence Research 21, no. 1 (2024): 104-135. 
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     * Обзор только литературных источников по базовым критериям (type, ML model, performance metrics and their value, etc.), но очень объемлющий (более 300 ссылок)      * Обзор только литературных источников по базовым критериям (type, ML model, performance metrics and their value, etc.), но очень объемлющий (более 300 ссылок) 
  
- **GAN для поиска аномалий**+==== GAN для поиска аномалий ====
  
   * Di Mattia, Federico, Paolo Galeone, Michele De Simoni, and Emanuele Ghelfi. [[https://arxiv.org/pdf/1906.11632|"A survey on gans for anomaly detection."]] arXiv preprint arXiv:1906.11632 (2019).    * Di Mattia, Federico, Paolo Galeone, Michele De Simoni, and Emanuele Ghelfi. [[https://arxiv.org/pdf/1906.11632|"A survey on gans for anomaly detection."]] arXiv preprint arXiv:1906.11632 (2019). 
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       * [[https://www.toolify.ai/ai-news/advanced-anomaly-detection-with-bigans-for-image-data-1284376|Advanced Anomaly Detection with BiGANs for Image Data]]        * [[https://www.toolify.ai/ai-news/advanced-anomaly-detection-with-bigans-for-image-data-1284376|Advanced Anomaly Detection with BiGANs for Image Data]] 
  
-**Поиск аномальных данных IACT**+==== Поиск аномальных данных IACT ====
  
   * De, Songshaptak, Writasree Maitra, Vikram Rentala, and Arun M. Thalapillil. [[https://arxiv.org/pdf/2206.05296|"Deep learning techniques for imaging air Cherenkov telescopes."]] Physical Review D 107, no. 8 (2023): 083026.   * De, Songshaptak, Writasree Maitra, Vikram Rentala, and Arun M. Thalapillil. [[https://arxiv.org/pdf/2206.05296|"Deep learning techniques for imaging air Cherenkov telescopes."]] Physical Review D 107, no. 8 (2023): 083026.
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   * 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]]   * 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 ===== ===== Гамма астрономия =====
 +
 +  * Очень красивая презентация [[https://gammalearn.pages.in2p3.fr/talks/20250128-ml-cosmic-workshop/|GammaLearn - Deep Learning for the CTAO event reconstruction]]
 +  * D. Bose, V. R. Chitnis, P. Majumdar, and B. S.Acharya. {{ :ml4gamma:s11734-021-00396-3-ground_based_gra.pdf |Ground-based gamma-ray astronomy: history and development of techniques}}
   * 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   * 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]]   * 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]]
  
  
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