dlcp2023:proceedings
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| dlcp2023:proceedings [05/10/2023 22:19] – [Plenary Reports] admin | dlcp2023:proceedings [18/01/2024 16:16] (current) – admin | ||
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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. | ||
| - | {{: | + | |
| 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**// | ||
| + | <color red>// | ||
| - | ==== Plenary Reports ==== | + | ===== Plenary Reports |
| | **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 ==== | + | ===== Track 1. Machine Learning in Fundamental Physics |
| - | + | ||
| - | | **Ju.Dubenskaya | + | | **Ju.Dubenskaya**. Generating Synthetic Images of Gamma-Ray Events for Imaging Atmospheric Cherenkov Telescopes Using Conditional Generative Adversarial Networks || |
| | **R.R.Fitagdinov**. Generation of the ground detector readings of the Telescope Array experiment and the search for anomalies using neural networks || | | **R.R.Fitagdinov**. Generation of the ground detector readings of the Telescope Array experiment and the search for anomalies using neural networks || | ||
| | **K.A.Galaktionov** / Neural network approach to impact parameter estimation in high-energy collisions using the microchannel plate detector data || | | **K.A.Galaktionov** / Neural network approach to impact parameter estimation in high-energy collisions using the microchannel plate detector data || | ||
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| | **A.D.Zaborenko**. Novelty Detection Neural Networks for Model-Independent New Physics Search | | **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 |
| | **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 | ||
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| | **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 |
| | **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 | ||
dlcp2023/proceedings.1696533580.txt.gz · Last modified: 05/10/2023 22:19 by admin