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dlcp2024:abstracts

dlcp2024:abstracts

Сборник аннотаций

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Секция 1. Машинное обучение в фундаментальной физике

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32. Machine Learning in Gamma Astronomy

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34. Calibrating for the Future: Enhancing Calorimeter Longevity with Deep Learning

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57. Методы машинного обучения в задаче предсказания дифференциальных сечений и структурных функций электророждения пиона на протоне в резонансной области.

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43. Gamma/hadron separation in the TAIGA experiment with neural network methods

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57. Boosting Novelty Detection Neural Networks with Rational Activations

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67. Кодирование входных сигналов в терминах ориентированных подграфорв в спайковой нейронной сети с локальной пластичностью.

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46. Определение направления ШАЛ по данным TAIGA HiSCORE с помощью полносвязных нейросетей

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41. Морфологическая классификация джетов активных ядер галактик

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42. Application of Kolmogorov-Arnold Networks in high energy physics

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44. Генерация показаний наемных детекторов и поиск аномалий в данных с помощью нейронных сетей

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хх. Нейросетевое моделирование оптических солитонов, описываемых обобщенными нелинейными уравнениями Шредингера

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25. Реконструкция ШАЛ, зарегистрированных флуоресцентными телескопами, с помощью нейронных сетей

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46. Определение направления ШАЛ по данным TAIGA HiSCORE с помощью полносвязных нейросетей

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45. Применение нейронных сетей для вычисления континуальных интегралов в квантовой теории

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40. Исследование механизма генерации гамма-вспышек в блазарах

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70. Многомерная оптимизация детекторных систем на примере мюонной защиты в эксперименте SHiP

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39. Решение задач математической физики на сетях радиальных базисных функций

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58. Enhanced Image Clustering with Random-Bond Ising Models Using LDPC Graph Representations and Nishimori Temperature Estimation

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Секция 2. Машинное обучение в науках об окружающей среде

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9.Сравнительный анализ методов машинного и глубокого обучения в задаче классификации волновых форм полного электронного содержания

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72. Application of machine learning methods for analyzing data from semiconductor gas sensors in dynamic temperature mode.

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64. ПРОГНОЗИРОВАНИЕ ХАРАКТЕРИСТИК ВОЗВРАТНОЙ МИГРАЦИИ НЕРКИ В УСТЬЕ РЕКИ ФРЕЙЗЕР С ПРИМЕНЕНИЕМ МЕТОДОВ ГЛУБОКОГО ОБУЧЕНИЯ

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56. Обзор поточечных и комплексных мер качества в исследованиях атмосферы и океана

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36. Статистическое прогнозирование уровня загрязнения воздуха частицами PM10 и PM2.5 в городских агломерациях в условиях сложного рельефа с применением методов машинного обучения на примере г. Гренобль

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49. Аппроксимация пространственно-временной изменчивости городского острова тепла Москвы методами машинного обучения

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3. Опыт использования методов машинного обучения в задачах диагностирования геоиндуцированных токов в высокоширотных энергосистемах

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72. Application of machine learning methods for analyzing data from semiconductor gas sensors in dynamic temperature mode

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49. Машинное обучение и глубокое обучение в науках об атмосфере, океане и климате: достижения и перспективы в 2024 году

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71.Towards the Statistical Correction of High-Resolution Weather Forecasts from the WRF Model employing Deep Learning

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74. The Ob-Yenisei plume movement analysis in the Kara Sea using artificial neural networks

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72. Прогнозирование состояния магнитосферы Земли с помощью специального алгоритма для работы с многомерными временными рядами

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36. Методы вероятностного программирования при реконструкции событий многоканального изображающего детектора: ЭЛЬФЫ и ТРЕКИ

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71. Применение методов машинного обучения для идентификации полярных мезомасштабных циклонов в данных численного моделирования атмосферы

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Секция 3. Машинное обучение в естественных науках

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47. Предсказание структуры дефектов MoS2 по заданным свойствам

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61. Estimation of signals in white noise using neural networks modeling

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37. Архитектура "трансформер" для анализа рисков группового влияния нутриентов пищи

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68. Метод сегментации очагов опухолевого поражения глаза ретинобластомой на основе глубокого обучения

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73. Nonlinear relevance estimation of multicollinear features for reducing the input dimensionality of optical spectroscopy inverse problem

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69. Use of Neural Network Approximation of the Parameters-Property Relationship in Synthesis of Carbon Dots

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62. Improving Representativity of Spectroscopic Data using Variational Autoencoders: Approaches and Problems

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68. Астрофизика частиц и анализ экспериментальных данных

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65. Comparative Analysis of the Procedures to Forecast the Kp Geomagnetic Index by Machine Learning

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50. Сети глубокого обучения для построения виртуальных датчиков технологических процессов нефтепереработки

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37. Нейронные операторы для гидродинамического моделирования подземных хранилищ газа (ПХГ)

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46. Модель машинного обучения на гауссовских процессах для предсказания энерговыделения в топливных ячейках активной зоны исследовательского ядерного реактора

View between have the of to through outside up who. Am himself and few not no dark owner of. As growing and in mark danger men experience engaged up should of.

47. An effective algorithm for predicting human fatigue using a portable Brain-Computer Interface

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55. Алгоритм классификации премоторных потенциалов по сигналу электроэнцефалограммы для нейрореабилитации с помощью интерфейса «мозг-компьютер» замкнутого цикла

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75. Spiking neural network actor-critic reinforcement learning with temporal coding and reward modulated plasticity

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48. Сверточные нейронные сети для создания углеродного фотолюминесцентного наносенсора ионов металлов

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73. Kolmogorov-Arnold Networks vs Multi-Layer Perceptron: Solution of an Inverse Problem of Exploration Geophysics

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67. Модификация визуального трансформера методом b-cos для повышения интерпретируемости в задаче классификации изображений дерматоскопии

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73. Machine Learning for NICA SPD Aerogel Reconstruction

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52. Improving Physics-Informed Neural Networks via Quasi-classical Loss Functionals

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42. Применение свёрточных нейронных сетей для выделения событий ШАЛ в эксперименте СФЕРА-3

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40. Дескрипторы для определения локальной структуры атомистических моделей аморфных льдов

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71. Deep learning-based eye anatomical structures synthetic images generating: a comparative analysis of techniques and models

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47. Engineering Point Defects in Transition Metal Dichalcogenides for Tailored Material Properties

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51. Машинное обучение и 3D анализ сцен в оценке сложноизмеряемых геометрических характеристик тела человека для задач биомедицины

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