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dlcp2023:program [26/06/2023 14:13] – [Poster section] stepanovadlcp2023:program [24/08/2023 13:07] 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.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    | {{:dlcp2023:dlcp-23_dolenko.pdf |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}} ||
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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 }}||
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 ===== Poster section ===== ===== Poster section =====
  
-  * V.Kalninsky, SPbSU, St.-Petersburg \\ {{:dlcp2023:kalnitsky.pdf |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, 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 }}.   * 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.txt · Last modified: 24/08/2023 17:36 by admin