dlcp2023:proceedings
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| ====== Proceedings ====== | ====== Proceedings ====== | ||
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| + | **//Jan. 18, 2024//** | ||
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| + | {{: | ||
| + | Вышел номер Вестника МГУ с трудами конференции: | ||
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| + | ---- | ||
| The proceedings of the DLCP2023 conference will be published as a special issue of the journal [[http:// | The proceedings of the DLCP2023 conference will be published as a special issue of the journal [[http:// | ||
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| After blind peer review, all accepted papers will be published in the conference proceedings. | After blind peer review, all accepted papers will be published in the conference proceedings. | ||
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| Notification of paper acceptance — < | Notification of paper acceptance — < | ||
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| More details can be found at [[http:// | More details can be found at [[http:// | ||
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| + | ===== Current status ===== | ||
| + | {{: | ||
| + | * Авторам разосланы гранки. Окончательная версия должна поступить в редакцию не позднее **4 декабря 2023г.** | ||
| + | * В конце ноября будут разосланы верстки для окончательной правки. | ||
| + | * DOI статей должны быть известны в начале декабря. | ||
| + | * Желающие могут получить письмо из издательства о принятии статьи в печать. Для этого надо написать мне запрос по электроной почте [[kryukov@theory.sinp.msu.ru]]. | ||
| + | * Тексты статей на сайте издательства будут доступны в январе 2024г. | ||
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| Более подробно правила изложены в {{ : | Более подробно правила изложены в {{ : | ||
| + | /** | ||
| ===== Status of submitted articles ===== | ===== Status of submitted articles ===== | ||
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| - | //Paper status updates 1 time per day// | ||
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| - | <color red>// | ||
| Legend: | Legend: | ||
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| * Reject - The article was rejected | * Reject - The article was rejected | ||
| * Withdrawn - Withdrawn from publication or not received on time | * Withdrawn - Withdrawn from publication or not received on time | ||
| + | **/ | ||
| ====== Final version of the Proceedings ====== | ====== Final version of the Proceedings ====== | ||
| //**List of accepted papers**// | //**List of accepted papers**// | ||
| - | ====Plenary Reports==== | + | <color red>// |
| + | |||
| + | ===== Plenary Reports | ||
| - | ^ Corresponding Author and Article Title || | ||
| | **M.I.Petrovskiy**. DEEP LEARNING METHODS FOR THE TASKS OF CREATING " | | **M.I.Petrovskiy**. DEEP LEARNING METHODS FOR THE TASKS OF CREATING " | ||
| - | ==== Track 1. Machine Learning in Fundamental Physics ==== | ||
| - | ^ Corresponding Author and Article Title || | + | ===== Track 1. Machine Learning in Fundamental Physics ===== |
| - | | Ju.Dubenskaya | + | |
| - | | R.R.Fitagdinov. Generation of the ground detector readings of the Telescope Array experiment and the search for anomalies using neural networks || | + | | **Ju.Dubenskaya**. Generating Synthetic Images of Gamma-Ray Events for Imaging Atmospheric Cherenkov Telescopes Using Conditional Generative Adversarial Networks || |
| - | | K.A.Galaktionov / Neural network approach to impact parameter estimation in high-energy collisions using the microchannel plate detector data || | + | | **R.R.Fitagdinov**. Generation of the ground detector readings of the Telescope Array experiment and the search for anomalies using neural networks || |
| - | | E.O.Gres. The selection of gamma events from IACT images with deep learning methods | + | | **K.A.Galaktionov** / Neural network approach to impact parameter estimation in high-energy collisions using the microchannel plate detector data || |
| - | | A.Kryukov. Preliminary results of convolutional neural network models in HiSCORE experiment | + | | **E.O.Gres**. The selection of gamma events from IACT images with deep learning methods |
| - | | A.Kryukov. The use of conditional variational autoencoders for simulation of EASs images from IACTs || | + | | **A.Kryukov**. Preliminary results of convolutional neural network models in HiSCORE experiment |
| - | | V.S.Latypova / Method for separating extensive air showers by primary mass using machine learning for a SPHERE-type Cherenkov telescope | + | | **A.Kryukov**. The use of conditional variational autoencoders for simulation of EASs images from IACTs || |
| - | | A.Y.Leonov. Deep Learning for Angle of Arrival Prediction in the Baikal Neutrino Telescope | + | | **V.S.Latypova** / Method for separating extensive air showers by primary mass using machine learning for a SPHERE-type Cherenkov telescope |
| - | | A.V. Matseiko. Application of machine learning methods in Baikal-GVD: | + | | **A.Y.Leonov**. Deep Learning for Angle of Arrival Prediction in the Baikal Neutrino Telescope |
| - | | A.D.Zaborenko. Novelty Detection Neural Networks for Model-Independent New Physics Search | + | | **A.V. Matseiko**. Application of machine learning methods in Baikal-GVD: |
| + | | **A.D.Zaborenko**. Novelty Detection Neural Networks for Model-Independent New Physics Search | ||
| - | ==== Track 2. Machine Learning in Natural Sciences ==== | + | ===== Track 2. Machine Learning in Natural Sciences |
| - | ^ Corresponding Author and Article Title || | + | | **M.Borisov**. Estimating cloud base height from all-sky imagery using artificial neural networks |
| - | | M.Borisov. Estimating cloud base height from all-sky imagery using artificial neural networks | + | | **S.Dolenko**(А.Guskov) Transfer Learning for Neural Network Solution of an Inverse Problem in Optical Spectroscopy |
| - | | S.Dolenko(А.Guskov) Transfer Learning for Neural Network Solution of an Inverse Problem in Optical Spectroscopy | + | | **I.M.Gadzhiev**. Classification Approach to Prediction of Geomagnetic Disturbances |
| - | | I.M.Gadzhiev. Classification Approach to Prediction of Geomagnetic Disturbances | + | | **V.Golikov**. 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 |
| - | | V.Golikov. 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 | + | | **A.Kasatkin**. Machine learning techniques for anomaly detection in high-frequency time series of wind speed and greenhouse gas concentration measurements |
| - | | A.Kasatkin. Machine learning techniques for anomaly detection in high-frequency time series of wind speed and greenhouse gas concentration measurements | + | | **I.Khabutdinov**. Identifying cetacean mammals in high-resolution optical imagery using anomaly detection approach employing Machine Learning models |
| - | | I.Khabutdinov. Identifying cetacean mammals in high-resolution optical imagery using anomaly detection approach employing Machine Learning models | + | | **M.Krinitsky**. Estimating significant wave height from X-band navigation radar using convolutional neural networks |
| - | | M.Krinitsky. Estimating significant wave height from X-band navigation radar using convolutional neural networks | + | | **M.A.Ledovskikh**. Recognition of skin lesions from image || |
| - | | M.A.Ledovskikh. Recognition of skin lesions from image || | + | | **A.V.Orekhov**. Unsupervised Machine Learning Methods for Determining Special Points of the Polymerase Chain Reaction Fluorescence Accumulation Curve || |
| - | | A.V.Orekhov. Unsupervised Machine Learning Methods for Determining Special Points of the Polymerase Chain Reaction Fluorescence Accumulation Curve || | + | | **S.A.Pavlov**. Application of Machine Learning Methods to Numerical Simulation of Hypersonic Flow || |
| - | | S.A.Pavlov. Application of Machine Learning Methods to Numerical Simulation of Hypersonic Flow || | + | | **V.Y.Rezvov**. Improvement of the AI-based estimation of signifi cant wave height based on preliminary training on synthetic X-band radar sea clutter images |
| - | | V.Y.Rezvov. Improvement of the AI-based estimation of signifi cant wave height based on preliminary training on synthetic X-band radar sea clutter images | + | | **O.E.Sarmanova**. Decoding fluorescence excitation-emission matrices of carbon dots aqueous solutions with convolutional neural networks to create multimodal nanosensor of metal ions || |
| - | | O.E.Sarmanova. Decoding fluorescence excitation-emission matrices of carbon dots aqueous solutions with convolutional neural networks to create multimodal nanosensor of metal ions || | + | | **A.Savin**. SMAP sea surface salinity improvement in the Arctic region using machine learning approaches |
| - | | A.Savin. SMAP sea surface salinity improvement in the Arctic region using machine learning approaches | + | | **A.Tyshko**. Automatic detection of acoustic signals from white whales and bottle-nosed dolphins |
| - | | A.Tyshko. Automatic detection of acoustic signals from white whales and bottle-nosed dolphins | + | | **A.V. Vorobev**. Machine learning for diagnostics of space weather effects in the Arctic region |
| - | | A.V. Vorobev. Machine learning for diagnostics of space weather effects in the Arctic region | + | |
| - | ==== Track 3. Modern Machine Learning Methods ==== | + | ===== Track 3. Modern Machine Learning Methods |
| - | ^ Corresponding Author and Article Title || | + | | **N.Y.Bykov** / Methods for a Partial Differential Equation Discovery: Application to Physical and Engineering Problems |
| - | | N.Y.Bykov / Methods for a Partial Differential Equation Discovery: Application to Physical and Engineering Problems | + | | **S.Dolenko**. Decomposition of Spectral Contour into Gaussian Bands using Improved Modification of Gender Genetic Algorithm |
| - | | S.Dolenko. Decomposition of Spectral Contour into Gaussian Bands using Improved Modification of Gender Genetic Algorithm | + | | **I.Isaev**. 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 |
| - | | I.Isaev. 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 | + | | **D.N.Polyakov** / Hyper-parameter tuning of neural network for high-dimensional problems in the case of Helmholtz equation |
| - | | D.N.Polyakov / Hyper-parameter tuning of neural network for high-dimensional problems in the case of Helmholtz equation | + | |
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dlcp2023/proceedings.1696533203.txt.gz · Last modified: 05/10/2023 22:13 by admin