Differences

This shows you the differences between two versions of the page.

--- dlcp2021:restricted:main [05/03/2025 14:33] – ↷ Page moved from dlcp:dlcp2021:restricted:main to dlcp2021:restricted:main admin
+++ dlcp2021:restricted:main [05/03/2025 14:33] (current) – ↷ Links adapted because of a move operation admin
@@ Line 15: / Line 15: @@
 ^^ Status  ^ Author, Article title ^ Qual./Agr. ||
-|| yes  | **P.Koundal**, IAP , KIT Karlsruhe \\ [[dlcp:dlcp2021:abstracts#Graph Neural Networks and application for Cosmic-Ray Analysis|Graph Neural Networks and application for Cosmic-Ray Analysis]]| good/yes ||
+|| yes  | **P.Koundal**, IAP , KIT Karlsruhe \\ [[dlcp2021:abstracts#Graph Neural Networks and application for Cosmic-Ray Analysis|Graph Neural Networks and application for Cosmic-Ray Analysis]]| good/yes ||
-|| yes  | **E.Gres**, ISU, Irkutsk \\ A.Kryukov, SINP MSU \\  [[dlcp:dlcp2021:abstracts#The preliminary results on analysis of TAIGA-IACT images using Convolutional Neural Networks|The preliminary results on analysis of TAIGA-IACT images using Convolutional Neural Networks]]| good/yes ||
+|| yes  | **E.Gres**, ISU, Irkutsk \\ A.Kryukov, SINP MSU \\  [[dlcp2021:abstracts#The preliminary results on analysis of TAIGA-IACT images using Convolutional Neural Networks|The preliminary results on analysis of TAIGA-IACT images using Convolutional Neural Networks]]| good/yes ||
-|| yes  | **M.Vasyutina**, Faculty of Physics, MSU \\ [[dlcp:dlcp2021:abstracts#Gamma/hadron separation for a ground based IACT (imaging atmospheric Cherenkov telescope) in experiment TAIGA using machine learning methods Random Forest|Gamma/hadron separation for a ground based IACT (imaging atmospheric Cherenkov telescope) in experiment TAIGA using machine learning methods Random Forest]]| not bad/yes ||
+|| yes  | **M.Vasyutina**, Faculty of Physics, MSU \\ [[dlcp2021:abstracts#Gamma/hadron separation for a ground based IACT (imaging atmospheric Cherenkov telescope) in experiment TAIGA using machine learning methods Random Forest|Gamma/hadron separation for a ground based IACT (imaging atmospheric Cherenkov telescope) in experiment TAIGA using machine learning methods Random Forest]]| not bad/yes ||
-|| yes  | **S.Polyakov**, SINP MSU \\ [[dlcp:dlcp2021:abstracts#Performance of convolutional neural networks processing simulated IACT images in the TAIGA experiment|Performance of convolutional neural networks processing simulated IACT images in the TAIGA experiment]]| good/yes ||
+|| yes  | **S.Polyakov**, SINP MSU \\ [[dlcp2021:abstracts#Performance of convolutional neural networks processing simulated IACT images in the TAIGA experiment|Performance of convolutional neural networks processing simulated IACT images in the TAIGA experiment]]| good/yes ||
-|| yes  | **A.Zaborenko**, Faculty of Physics, MSU \\ [[dlcp:dlcp2021:abstracts#Application of deep learning technique to an analysis of hard scattering processes at colliders|Application of deep learning technique to an analysis of hard scattering processes at colliders]]| good/yes ||
+|| yes  | **A.Zaborenko**, Faculty of Physics, MSU \\ [[dlcp2021:abstracts#Application of deep learning technique to an analysis of hard scattering processes at colliders|Application of deep learning technique to an analysis of hard scattering processes at colliders]]| good/yes ||
-|| yes  | **A.Vlaskina**, Faculty of Physics, MSU \\ [[dlcp:dlcp2021:abstracts#Using convolutional neural network for analysis of HiSCORE events|Using convolutional neural network for analysis of HiSCORE events]]| good/yes ||
+|| yes  | **A.Vlaskina**, Faculty of Physics, MSU \\ [[dlcp2021:abstracts#Using convolutional neural network for analysis of HiSCORE events|Using convolutional neural network for analysis of HiSCORE events]]| good/yes ||
-|| yes  | **V.Tokareva**, IAP KIT \\  [[dlcp:dlcp2021:abstracts#Using modern machine learning methods on KASCADE data for science and education|Using modern machine learning methods on KASCADE data for science and education]]| good/yes ||
+|| yes  | **V.Tokareva**, IAP KIT \\  [[dlcp2021:abstracts#Using modern machine learning methods on KASCADE data for science and education|Using modern machine learning methods on KASCADE data for science and education]]| good/yes ||
-|| yes  | **P.Bezyazeekov**, API ISU \\ [[dlcp:dlcp2021:abstracts#Legacy of Tunka-Rex software and data|Legacy of Tunka-Rex software and data]]| good/yes ||
+|| yes  | **P.Bezyazeekov**, API ISU \\ [[dlcp2021:abstracts#Legacy of Tunka-Rex software and data|Legacy of Tunka-Rex software and data]]| good/yes ||
-|| yes  | **Ju.Dubenskaya**, SINP MSU \\ [[dlcp:dlcp2021:abstracts#Modeling images of proton events for the TAIGA project using a generative adversarial network: features of the network architecture and the learning process|Modeling images of proton events for the TAIGA project using a generative adversarial network: features of the network architecture and the learning process]]| good/yes ||
+|| yes  | **Ju.Dubenskaya**, SINP MSU \\ [[dlcp2021:abstracts#Modeling images of proton events for the TAIGA project using a generative adversarial network: features of the network architecture and the learning process|Modeling images of proton events for the TAIGA project using a generative adversarial network: features of the network architecture and the learning process]]| good/yes ||
@@ Line 29: / Line 29: @@
 ^^ Starus  ^ Author, Article title ^ En.Qual./Agrr. ||
-||yes| **M.Krinitsky**, Shirshov Institute of Oceanology, RAS \\ [[dlcp:dlcp2021:abstracts#Identifying_partial_differential_equations_of_land_surface_schemes_in_INM_climate_models_with_neural_networks|Identifying partial differential equations of land surface schemes in INM climate models with neural networks]]| good/yes ||
+||yes| **M.Krinitsky**, Shirshov Institute of Oceanology, RAS \\ [[dlcp2021:abstracts#Identifying_partial_differential_equations_of_land_surface_schemes_in_INM_climate_models_with_neural_networks|Identifying partial differential equations of land surface schemes in INM climate models with neural networks]]| good/yes ||
-||yes| **A.Demichev**, SINP MSU \\ [[dlcp:dlcp2021:abstracts#Equivariant_Gaussian_Processes_as_Limiting_Convolutional Networks with Infinite Number of Channels|Equivariant Gaussian Processes as Limiting Convolutional Networks with Infinite Number of Channels]]| good/yes ||
+||yes| **A.Demichev**, SINP MSU \\ [[dlcp2021:abstracts#Equivariant_Gaussian_Processes_as_Limiting_Convolutional Networks with Infinite Number of Channels|Equivariant Gaussian Processes as Limiting Convolutional Networks with Infinite Number of Channels]]| good/yes ||
-||yes| **<del>I.Gadzhiev</del> S.Dolenko**, SINP MSU \\ [[dlcp:dlcp2021:abstracts#A_convolutional_hierarchical_neural_network_classifier|A convolutional hierarchical neural network classifier]]| good/yes ||
+||yes| **<del>I.Gadzhiev</del> S.Dolenko**, SINP MSU \\ [[dlcp2021:abstracts#A_convolutional_hierarchical_neural_network_classifier|A convolutional hierarchical neural network classifier]]| good/yes ||
-|| yes | **A.O.Efitorov**, SINP MSU \\ [[dlcp:dlcp2021:abstracts#Use of conditional generative adversarial networks to improve representativity of data in optical spectroscopy|Use of conditional generative adversarial networks to improve representativity of data in optical spectroscopy]]| good/yes ||
+|| yes | **A.O.Efitorov**, SINP MSU \\ [[dlcp2021:abstracts#Use of conditional generative adversarial networks to improve representativity of data in optical spectroscopy|Use of conditional generative adversarial networks to improve representativity of data in optical spectroscopy]]| good/yes ||
-|| yes | **I.Isaev**, SINP MSU \\ [[dlcp:dlcp2021:abstracts#Neural network solution of inverse problems of geological prospecting with discrete output|Neural network solution of inverse problems of geological prospecting with discrete output]]| good/yes ||
+|| yes | **I.Isaev**, SINP MSU \\ [[dlcp2021:abstracts#Neural network solution of inverse problems of geological prospecting with discrete output|Neural network solution of inverse problems of geological prospecting with discrete output]]| good/yes ||
-|| yes |**<del>A.Naumov</del> R.Rybka**, National Research Centre “Kurchatov Institute” \\ [[dlcp:dlcp2021:abstracts#The Russian language corpus and a neural network to analyse Internet tweet reports about Covid-19|The Russian language corpus and a neural network to analyse Internet tweet reports about Covid-19]]| not bad/yes ||
+|| yes |**<del>A.Naumov</del> R.Rybka**, National Research Centre “Kurchatov Institute” \\ [[dlcp2021:abstracts#The Russian language corpus and a neural network to analyse Internet tweet reports about Covid-19|The Russian language corpus and a neural network to analyse Internet tweet reports about Covid-19]]| not bad/yes ||
-|| yes |**A.A. Selivanov**(rybka2), NRC «Kurchatov Institute» \\ [[dlcp:dlcp2021:abstracts#EVALUATION OF MACHINE LEARNING METHODS FOR RELATION EXTRACTION BETWEEN DRUG ADVERSE EFFECTS AND MEDICATIONS IN RUSSIAN TEXTS OF INTERNET USER REVIEWS|EVALUATION OF MACHINE LEARNING METHODS FOR RELATION EXTRACTION BETWEEN DRUG ADVERSE EFFECTS AND MEDICATIONS IN RUSSIAN TEXTS OF INTERNET USER REVIEWS]]| good/yes ||
+|| yes |**A.A. Selivanov**(rybka2), NRC «Kurchatov Institute» \\ [[dlcp2021:abstracts#EVALUATION OF MACHINE LEARNING METHODS FOR RELATION EXTRACTION BETWEEN DRUG ADVERSE EFFECTS AND MEDICATIONS IN RUSSIAN TEXTS OF INTERNET USER REVIEWS|EVALUATION OF MACHINE LEARNING METHODS FOR RELATION EXTRACTION BETWEEN DRUG ADVERSE EFFECTS AND MEDICATIONS IN RUSSIAN TEXTS OF INTERNET USER REVIEWS]]| good/yes ||
-||yes|**N.V.Abasov**, Melentiev Energy Systems Institute SB RAS \\ [[dlcp:dlcp2021:abstracts#The technology of long-term forecasting of water inflow into reservoirs using a multi-parameter neural network|The technology of long-term forecasting of water inflow into reservoirs using a multi-parameter neural network]]| not bad/yes ||
+||yes|**N.V.Abasov**, Melentiev Energy Systems Institute SB RAS \\ [[dlcp2021:abstracts#The technology of long-term forecasting of water inflow into reservoirs using a multi-parameter neural network|The technology of long-term forecasting of water inflow into reservoirs using a multi-parameter neural network]]| not bad/yes ||
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