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dlcp2023:program [23/06/2023 21:41] – [June 23, 2023] stepanovadlcp2023:program [24/08/2023 17:36] (current) – admin
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 ^^ 11:15-11:45  ^ Welcome coffee  |  || ^^ 11:15-11:45  ^ Welcome coffee  |  ||
 || 11:45-12:00  | **Opening of the conference**  |  || || 11:45-12:00  | **Opening of the conference**  |  ||
-|| 12:00-12:30  | **L.Dudko** \\ MSU, Moscow  | **{{:dlcp2023:dlcp23_dudko.pdf |Methodology for the use of neural networks in the data analysis of the collider experiments}}**|| +|| 12:00-12:30  | **L.Dudko** \\ MSU, Moscow  | **{{:dlcp2023:dlcp23_dudko.pdf |Methodology for the use of neural networks in the data analysis of the collider experiments}}** \\ (Plenary report)|| 
-|| 12:30-12:45  | Ju.Dubenskaya \\ SINP MSU, Moscow  | {{:dlcp2023:dlcp2023-jdubenskaya.pdf |Generating Synthetic Images of Gamma-Ray Events for Imaging Atmospheric Cherenkov Telescopes Using Conditional Generative Adversarial Networks }}||+|| 12:30-12:45  | Ju.Dubenskaya \\ SINP MSU, Moscow  | {{:dlcp2023:dlcp2023-jdubenskaya.pdf |Generating Synthetic Images of Gamma-Ray Events for Imaging Atmospheric Cherenkov Telescopes Using Conditional Generative Adversarial Networks }} \\ (Invited report)||
 || 12:45-13:00  | R.Fitagdinov \\ MIPT, Moscow region; INR RAS, Moscow  | {{:dlcp2023:fitagdinov_max_aplitude_pres.pdf |Generation of the ground detector readings of the Telescope Array experiment and the search for anomalies using neural networks}}|| || 12:45-13:00  | R.Fitagdinov \\ MIPT, Moscow region; INR RAS, Moscow  | {{:dlcp2023:fitagdinov_max_aplitude_pres.pdf |Generation of the ground detector readings of the Telescope Array experiment and the search for anomalies using neural networks}}||
 || 13:00-13:15  | K.Galaktionov \\ SPbSU, St.Petersburg | {{:dlcp2023:galaktionov_nnimpparmcp.pdf |Neural network approach to impact parameter estimation in high-energy collisions using the microchannel plate detector data}}|| || 13:00-13:15  | K.Galaktionov \\ SPbSU, St.Petersburg | {{:dlcp2023:galaktionov_nnimpparmcp.pdf |Neural network approach to impact parameter estimation in high-energy collisions using the microchannel plate detector data}}||
 || 13:15-13:30  | E.Gres \\ IGU, Irkutsk | * {{:dlcp2023:dlcp_report_gres_1.pdf |The selection of rare gamma event from IACT images with deep learning methods}}|| || 13:15-13:30  | E.Gres \\ IGU, Irkutsk | * {{:dlcp2023:dlcp_report_gres_1.pdf |The selection of rare gamma event from IACT images with deep learning methods}}||
 ^^ 13:30-14:30  ^ LUNCH | || ^^ 13:30-14:30  ^ LUNCH | ||
-|| 14:30-15:00  | **A.Kryukov** \\ MSU, Moscow | **{{:dlcp2023:dl_in_iact_v4.pdf |Machine Learning in Gamma Astronomy}}**|| +|| 14:30-15:00  | **A.Kryukov** \\ MSU, Moscow | **{{:dlcp2023:dl_in_iact_v4.pdf |Machine Learning in Gamma Astronomy}}** \\ (Plenary report)|| 
-|| 15:00-15:15  | A.Kryukov \\ SINP MSU, Moscow | {{:dlcp2023:dlcp2023-vlaskina-v3.pdf |Preliminary results of convolutional neural network models in HiSCORE experiment}}||+|| 15:00-15:15  | A.Kryukov \\ SINP MSU, Moscow | {{:dlcp2023:dlcp2023-vlaskina-v3.pdf |Preliminary results of convolutional neural network models in HiSCORE experiment}} \\ (Invited report)||
 ||15:15-15:30| S.Pavlov \\ SPbSU, St.Petersburg | {{:dlcp2023:presentation__conference__2023_istomin_pavlov_last.pdf |Application of machine learning methods to numerical simulation of hypersonic flow}}|| ||15:15-15:30| S.Pavlov \\ SPbSU, St.Petersburg | {{:dlcp2023:presentation__conference__2023_istomin_pavlov_last.pdf |Application of machine learning methods to numerical simulation of hypersonic flow}}||
 ^^ 16:00-16:30  ^ Coffee Break | || ^^ 16:00-16:30  ^ Coffee Break | ||
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 || 16:45-17:00  | A.Matseiko \\ MIPT, Moscow region; INR RAS, Moscow | {{:dlcp2023:Мацейко_Альберт_dlcp23.pdf |Application of machine learning methods in Baikal-GVD: background noise rejection and selection of neutrino-induced events}} || || 16:45-17:00  | A.Matseiko \\ MIPT, Moscow region; INR RAS, Moscow | {{:dlcp2023:Мацейко_Альберт_dlcp23.pdf |Application of machine learning methods in Baikal-GVD: background noise rejection and selection of neutrino-induced events}} ||
 || 17:00-17:15  | A.Zaborenko \\ MSU, Moscow | {{:dlcp2023:dlcp2023_zaborenko.pdf |Novelty Detection Neural Networks for Model-Independent New Physics Search}} || || 17:00-17:15  | A.Zaborenko \\ MSU, Moscow | {{:dlcp2023:dlcp2023_zaborenko.pdf |Novelty Detection Neural Networks for Model-Independent New Physics Search}} ||
-|| 17:15-17:30  | A.Kryukov \\ SINP MSU, Moscow | {{:dlcp2023:dlcp2023_polyakov.pdf |The use of conditional variational autoencoders for simulation of EASs images from IACTs}}||+|| 17:15-17:30  | A.Kryukov \\ SINP MSU, Moscow | {{:dlcp2023:dlcp2023_polyakov-2.1.pdf |The use of conditional variational autoencoders for simulation of EASs images from IACTs}} \\ (Invited report)||
 || 17:30-17:45  | M.Borisov \\ MIPT, Moscow region  | {{:dlcp2023:Borisov_DLCP2023.pdf |Estimating cloud base height from all-sky imagery using artificial neural networks}}|| || 17:30-17:45  | M.Borisov \\ MIPT, Moscow region  | {{:dlcp2023:Borisov_DLCP2023.pdf |Estimating cloud base height from all-sky imagery using artificial neural networks}}||
 || ||
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 /**{{:dlcp2023:corrected.png?100|}}**/ /**{{:dlcp2023:corrected.png?100|}}**/
  
-|| 10:00-10:30  | **A.Boukhanovsly** \\ ITMO University, St.Petersburg | **{{ :dlcp2023:dlcp2023-boukhanovsky.pdf |Generative AI for large models and digital twins}}** \\ {{ :dlcp2023:dlcp2023-boukhanovsky.pptx |}}|| +|| 10:00-10:30  | **A.Boukhanovsly** \\ ITMO University, St.Petersburg | **{{ :dlcp2023:dlcp2023-boukhanovsky.pdf |Generative AI for large models and digital twins}}** \\ {{ :dlcp2023:dlcp2023-boukhanovsky.pptx |}} \\ (Plenary report)|| 
-|| 10:30-10:45  | S.Dolenko \\ SINP MSU, Moscow    | Decomposition of Spectral Contour into Gaussian Bands using Improved Modification of Gender Genetic Algorithm ||+|| 10:30-10:45  | S.Dolenko \\ SINP MSU, Moscow    | {{:dlcp2023:dlcp-23_dolenko.pdf |Decomposition of Spectral Contour into Gaussian Bands using Improved Modification of Gender Genetic Algorithm }} \\ (Invited report) ||
 || 10:45-11:00  | A.Hvatov \\ ITMO University, St.Petersburg | * {{:dlcp2023:dlcp_22.06_hvatov.pptx |Robust equation discovery as a machine learning method}} \\ {{ :dlcp2023:dlcp2023-hvatov.pdf |}}|| || 10:45-11:00  | A.Hvatov \\ ITMO University, St.Petersburg | * {{:dlcp2023:dlcp_22.06_hvatov.pptx |Robust equation discovery as a machine learning method}} \\ {{ :dlcp2023:dlcp2023-hvatov.pdf |}}||
 || 11:00-11:15  | N.Bykov \\ ITMO University, St.Petersburg  | {{:dlcp2023:bykov_dlcp_v5.pptx |Reconstruction Methods for a Partial Differential Equation: Application to Physical and Engineering Problems }} || || 11:00-11:15  | N.Bykov \\ ITMO University, St.Petersburg  | {{:dlcp2023:bykov_dlcp_v5.pptx |Reconstruction Methods for a Partial Differential Equation: Application to Physical and Engineering Problems }} ||
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 || 12:15-12:30 | M.Krinitsky \\ Shirshov Institute of Oceanology, RAS, Moscow  | {{ :dlcp2023:dlcp2023-krinitskiy_static.pdf |Estimating significant wave height from X-band navigation radar using convolutional neural networks}} {{ :dlcp2023:krinitskiyetal-dlcp2023.pptx |}}|| || 12:15-12:30 | M.Krinitsky \\ Shirshov Institute of Oceanology, RAS, Moscow  | {{ :dlcp2023:dlcp2023-krinitskiy_static.pdf |Estimating significant wave height from X-band navigation radar using convolutional neural networks}} {{ :dlcp2023:krinitskiyetal-dlcp2023.pptx |}}||
 || 12:30-12:45 | V.Golikov \\ MIPT, Moscow region  | * 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     || || 12:30-12:45 | V.Golikov \\ MIPT, Moscow region  | * 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     ||
-|| 12:45-13:00 | S.Dolenko \\ SINP MSU, Moscow   | {{ :dlcp2023:2023_dlcp_guskov.pdf |Transfer Learning for Neural Network Solution of an Inverse Problem in Optical Spectroscopy}}   ||+|| 12:45-13:00 | S.Dolenko \\ SINP MSU, Moscow   | {{ :dlcp2023:2023_dlcp_guskov.pdf |Transfer Learning for Neural Network Solution of an Inverse Problem in Optical Spectroscopy}}  \\ (Invited report)  ||
 || 13:00-13:15 | I.Isaev \\ SINP MSU, KIRE RAS, Moscow  | {{ :dlcp2023:isaev_dlcp-2023_presentation.pdf |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}} || || 13:00-13:15 | I.Isaev \\ SINP MSU, KIRE RAS, Moscow  | {{ :dlcp2023:isaev_dlcp-2023_presentation.pdf |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}} ||
 || 13:15-13:30 | A.Polyakov \\ SPbSU, St.Petersburg  | {{ :dlcp2023:dlcp2023-polyakov_spb.pptx |A technique for the total ozone columns retrieval using spectral measurements of the IKFS-2 instrument}} || || 13:15-13:30 | A.Polyakov \\ SPbSU, St.Petersburg  | {{ :dlcp2023:dlcp2023-polyakov_spb.pptx |A technique for the total ozone columns retrieval using spectral measurements of the IKFS-2 instrument}} ||
 ^^ 13:30-14:30  ^ LUNCH  |  || ^^ 13:30-14:30  ^ LUNCH  |  ||
-|| 14:30-15:00  | **A.Moskovsky** \\ RSC  | **{{:dlcp2023:rsc_group_dlcp_2023_rus_v1.pdf |High-performance computer systems for machine learning problems }}** ||+|| 14:30-15:00  | **A.Moskovsky** \\ RSC  | **High-performance computer systems for machine learning problems** ||
 ||15:00-15:15| M.Ledovskikh \\ SPbSU, St.Petersburg | *  {{:dlcp2023:мледовских.pdf |Recognition of skin lesions by images}} || ||15:00-15:15| M.Ledovskikh \\ SPbSU, St.Petersburg | *  {{:dlcp2023:мледовских.pdf |Recognition of skin lesions by images}} ||
 ^^ <color green>15:15</color> ^ [[:dlcp2023:social|Social event]] | See details [[:dlcp2023:social|here]]|| ^^ <color green>15:15</color> ^ [[:dlcp2023:social|Social event]] | See details [[:dlcp2023:social|here]]||
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 ===== June 23, 2023===== ===== June 23, 2023=====
  
-||10:00-10:30| **M.Petrovsky** \\ MSU, Moscow | **{{:dlcp2023:petrovskiy.pptx |Deep learning methods for the tasks of creating "digital twins" for technological processes }}**  ||+||10:00-10:30| **M.Petrovsky** \\ MSU, Moscow | **{{:dlcp2023:petrovskiy.pptx |Deep learning methods for the tasks of creating "digital twins" for technological processes }}**  \\ (Plenary report) ||
 || 10:30-10:45  | A.Savin \\ MIPT, Moscow region; Shirshov Institute of Oceanology, RAS, Moscow | {{:dlcp2023:dlcp_savin_2023.pdf |SMAP sea surface salinity improvement in the Arctic region using machine learning approaches }} || || 10:30-10:45  | A.Savin \\ MIPT, Moscow region; Shirshov Institute of Oceanology, RAS, Moscow | {{:dlcp2023:dlcp_savin_2023.pdf |SMAP sea surface salinity improvement in the Arctic region using machine learning approaches }} ||
 ||10:45-11:00| A.Orekhov \\ SPbSU, St.Petersburg | {{:dlcp2023:слайдыdlcp2023_орехов.pdf |Unsupervised machine learning methods for determination of critical points of the fluorescence accumulation curve for real-time polymerase chain reaction }}|| ||10:45-11:00| A.Orekhov \\ SPbSU, St.Petersburg | {{:dlcp2023:слайдыdlcp2023_орехов.pdf |Unsupervised machine learning methods for determination of critical points of the fluorescence accumulation curve for real-time polymerase chain reaction }}||
-||11:00-11:15| A.Vasiliev \\ MSU, AI, Moscow | * 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 ||+||11:00-11:15| A.Vasiliev \\ MSU, AI, Moscow | * {{:dlcp2023:dlcp2023_vasiliev.pdf |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 }}||
 ||11:15-11:30| Z.Kurdoshev \\ Tomsk State University, Tomsk | * {{:dlcp2023:Kurdoshev.pdf |The importance of the number of overfits in time series forecasting by some optimizers and loss functions in neural networks }}|| ||11:15-11:30| Z.Kurdoshev \\ Tomsk State University, Tomsk | * {{:dlcp2023:Kurdoshev.pdf |The importance of the number of overfits in time series forecasting by some optimizers and loss functions in neural networks }}||
 ||11:30-11:45| A.Tyshko \\ Shirshov Institute of Oceanology, RAS, Moscow | * {{:dlcp2023:dlcp_2023_тышко.pptx |Automatic detection of acoustic signals from white whales and bottle-nosed dolphins }} || ||11:30-11:45| A.Tyshko \\ Shirshov Institute of Oceanology, RAS, Moscow | * {{:dlcp2023:dlcp_2023_тышко.pptx |Automatic detection of acoustic signals from white whales and bottle-nosed dolphins }} ||
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 ||12:15-12:30| I.Khabutdinov \\ Shirshov Institute of Oceanology, RAS, Moscow  | * {{:dlcp2023:dlcp_Khabutdinov.pptx |Identifying cetacean mammals in high-resolution optical imagery using anomaly detection approach employing Machine Learning models }}|| ||12:15-12:30| I.Khabutdinov \\ Shirshov Institute of Oceanology, RAS, Moscow  | * {{:dlcp2023:dlcp_Khabutdinov.pptx |Identifying cetacean mammals in high-resolution optical imagery using anomaly detection approach employing Machine Learning models }}||
 ||12:30-12:45 | M.Zotov \\ SINP MSU, Moscow| * {{dlcp2023:zotov-dlcp23.pdf |Search for Meteors in the Mini-EUSO Orbital Telescope Data with Neural Networks }}|| ||12:30-12:45 | M.Zotov \\ SINP MSU, Moscow| * {{dlcp2023:zotov-dlcp23.pdf |Search for Meteors in the Mini-EUSO Orbital Telescope Data with Neural Networks }}||
-||12:45-13:00| A.Vorobev \\ Geophysical Center RAS, Moscow | * Machine learning for diagnostics of space weather effects in the Arctic region  || +||12:45-13:00| A.Vorobev \\ Geophysical Center RAS, Moscow | * {{:dlcp2023:vorobev_dlcp’2023.pptx |Machine learning for diagnostics of space weather effects in the Arctic region  }}|| 
-||13:00-13:15| V.Rezvov \\ Shirshov Institute of Oceanology, RAS, Moscow | * Improving the accuracy of the neural network estimation of meaningful height of wind waves based on ship navigation radar data by means of preliminary training on synthetic data ||+||13:00-13:15| V.Rezvov \\ Shirshov Institute of Oceanology, RAS, Moscow | * {{:dlcp2023:Rezvov_DLCP-2023.pdf |Improving the accuracy of the neural network estimation of meaningful height of wind waves based on ship navigation radar data by means of preliminary training on synthetic data }}||
 ||13:15-13:30| A.Kasatkin \\ Shirshov Institute of Oceanology, RAS, Moscow  | * {{:dlcp2023:акасаткин_доклад.pptx |Machine learning techniques for anomaly detection in high-frequency time series of wind speed and greenhouse gas concentration measurements }} || ||13:15-13:30| A.Kasatkin \\ Shirshov Institute of Oceanology, RAS, Moscow  | * {{:dlcp2023:акасаткин_доклад.pptx |Machine learning techniques for anomaly detection in high-frequency time series of wind speed and greenhouse gas concentration measurements }} ||
 || 13:30-13:45| V.Latypova \\ SINP MSU, Moscow | {{:dlcp2023:latypova_dlcp.pdf |A universal method for separating extensive air showers by primary mass using machine learning for a Cherenkov telescope of the SPHERE type}} || || 13:30-13:45| V.Latypova \\ SINP MSU, Moscow | {{:dlcp2023:latypova_dlcp.pdf |A universal method for separating extensive air showers by primary mass using machine learning for a Cherenkov telescope of the SPHERE type}} ||
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 ===== Poster section ===== ===== Poster section =====
  
-  * V.Kalninsky, SPbSU, St.-Petersburg \\ Modification of soft connectives in machine learning models +  * V.Kalnitsky, SPbSU, St.-Petersburg \\ {{:dlcp2023:kalnitsky.pdf |Modification of soft connectives in machine learning models }}  
-  * O.Sarmanova (SINP MSU) \\ Decoding fluorescence excitation-emission matrices of carbon dots aqueous solutions with convolutional neural networks to create multimodal nanosensor of metal ions.+  * O.Sarmanova, SINP MSU, Moscow \\ {{:dlcp2023:dlcp_2023_poster_sarmanova.pdf |Decoding fluorescence excitation-emission matrices of carbon dots aqueous solutions with convolutional neural networks to create multimodal nanosensor of metal ions }}.
  
  
dlcp2023/program.1687545673.txt.gz · Last modified: 23/06/2023 21:41 by stepanova