dlcp2023:abstracts
Book of Abstracts
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Plenary reports
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Генеративный ИИ для больших моделей и цифровых двойников
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Methodology for the use of neural networks in the data analysis of the collider experiments
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Machine Learning in Gamma Astronomy
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Методы глубокого обучения для задач построения «цифровых двойников» технологических процессов
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Высокопроизводительные компьютерные системы в задачах машинного обучения
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Track 1. Machine Learning in Fundamental Physics
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23. Generating Synthetic Images of Gamma-Ray Events for Imaging Atmospheric Cherenkov Telescopes Using Conditional Generative Adversarial Networks
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8. Generation of the ground detector readings of the Telescope Array experiment and the search for anomalies using neural networks
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40. Neural network approach to impact parameter estimation in high-energy collisions using the microchannel plate detector data
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28. The selection of rare gamma event from IACT images with deep learning methods
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6. Preliminary results of neural network models in HiSCORE experiment
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27. The use of conditional variational autoencoders for simulation of EASs images from IACTs
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15. Универсальный метод разделения широких атмосферных ливней по первичной массе с помощью машинного обучения для черенковского телескопа типа СФЕРА
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7. Using Neural Networks for Reconstructing Particle Arrival Angles in the Baikal-GVD Neutrino Telescope
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9. Application of machine learning methods in Baikal-GVD: background noise rejection and selection of neutrino-induced events
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36. Novelty Detection Neural Networks for Model-Independent New Physics Search
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Track 2. Modern Machine Learning Methods
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14. Reconstruction Methods for a Partial Differential Equation: Application to Physical and Engineering Problems
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45. Decomposition of Spectral Contour into Gaussian Bands using Improved Modification of Gender Genetic Algorithm
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3. Robust equation discovery as a machine learning method
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20. Modification of soft connectives in machine learning models
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26. The importance of the number of overfits in time series forecasting by some optimizers and loss functions in neural networks.
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21. Training by relaxation of Boltzmann machine with multispin interactions
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17. Hyper-parameter tuning of neural network for high-dimensional problems in the case of Helmholtz equation
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Track 3. Machine Learning in Natural Sciences
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12. Estimating cloud base height from all-sky imagery using artificial neural networks
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46. Classification Approach to Prediction of Geomagnetic Disturbances
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50. CLIENT-SERVER APPLICATION FOR AUTOMATED ESTIMATION OF THE MATERIAL COMPOSITION OF BOTTOM SEDIMENTS IN THE >0.1 MM FRACTION FROM MICROPHOTOGRAPHY USING MODERN DEEP LEARNING METHODS
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49. Transfer Learning for Neural Network Solution of an Inverse Problem in Optical Spectroscopy.
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48. The study of the integration of physical methods in the neural network solution of the inverse problem of exploration geophysics with variable physical properties of the medium.
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13. Estimating significant wave height from X-band navigation radar using convolutional neural networks
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38. Machine learning techniques for anomaly detection in high-frequency time series of wind speed and greenhouse gas concentration measurements
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42. Identifying cetacean mammals in high-resolution optical imagery using anomaly detection approach employing Machine Learning models
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41. Recognition of skin lesions by images
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44. Определение особых точек кривой накопления флуоресценции полимеразной цепной реакции методами машинного обучения без учителя
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10. Application of machine learning methods to numerical simulation of hypersonic flow
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24. A technique for the total ozone columns retrieval using spectral measurements of the IKFS-2 instrument.
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24. Повышение точности нейросетевой оценки значимой высоты ветрового волнения по данным судового навигационного радара за счет предварительного обучения на синтетических данных
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Decoding fluorescence excitation-emission matrices of carbon dots aqueous solutions with convolutional neural networks to create multimodal nanosensor of metal ions
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34. SMAP sea surface salinity improvement in the Arctic region using machine learning approaches
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33. Determination of the charge of molecular fragments by machine learning methods
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18. АВТОМАТИЧЕСКОЕ ДЕТЕКТИРОВАНИЕ АКУСТИЧЕСКИХ СИГНАЛОВ БЕЛУХ И АФАЛИН
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37. The role of artificial intelligence in the preparation of modern scientific and pedagogical staff. The experience of the course "Neural networks and their application in scientific research" of Moscow State University named after M. V. Lomonosov.
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31. Machine learning for diagnostics of space weather effects in the Arctic region
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30. Search for Meteors in the Mini-EUSO Orbital Telescope Data with Neural Networks
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