dlcp21:abstracts
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dlcp21:abstracts [21/06/2021 22:59] – admin | dlcp21:abstracts [22/06/2021 19:54] (current) – [Equivariant Gaussian Processes as Limiting Convolutional Networks with Infinite Number of Channels] admin | ||
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- | **N.V. Abasov, Melentiev Energy Systems Institute SB RAS** \\ | + | **N.V. Abasov, Melentiev Energy Systems Institute SB RAS, Russia** \\ |
- | E.N. Osipchuk, Melentiev Energy Systems Institute SB RAS \\ | + | E.N. Osipchuk, Melentiev Energy Systems Institute SB RAS, Russia |
- | V.M. Berdnikov, Melentiev Energy Systems Institute SB RAS | + | V.M. Berdnikov, Melentiev Energy Systems Institute SB RAS, Russia |
//Short presentation (15 min)// | //Short presentation (15 min)// | ||
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===== Legacy of Tunka-Rex software and data. ===== | ===== Legacy of Tunka-Rex software and data. ===== | ||
- | **Pavel Bezyazeekov, | + | **Pavel Bezyazeekov, |
Tunka-Rex is a digital antenna array for measuring the radio emission from air-showers, | Tunka-Rex is a digital antenna array for measuring the radio emission from air-showers, | ||
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===== Equivariant Gaussian Processes as Limiting Convolutional Networks with Infinite Number of Channels ===== | ===== Equivariant Gaussian Processes as Limiting Convolutional Networks with Infinite Number of Channels ===== | ||
- | **A.Demichev, | + | **A.Demichev, |
//Short presentation (15 min)// | //Short presentation (15 min)// | ||
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The topic within which this work was carried out is related to the establishment of relationships between various methods of machine learning (ML). The ultimate goal of establishing such interrelations is to achieve a better theoretical understanding of these methods and their improvement. In particular, a correspondence has recently been established between the appropriate asymptotics of deep neural networks (DNNs), including convolutional ones (CNNs), and the ML method based on Gaussian processes. Since Gaussian processes are mathematically equivalent to free (Euclidean) quantum field theory (QFT), one of the intriguing consequences of these relationships is the potential for using a broad range of QFT methods for analyzing DNNs. There are evidences (including experimental) that non-asymptotic (that is, implementable in practice) DNNs correspond to QFT with interactions. An important feature of convolutional networks is their equivariance (consistency) with respect to the symmetry transformations of the input data. In this work, we establish a relationship between the many-channel limit of equivariant CNNs and the corresponding equivariant Gaussian process (GP), and hence the QFT with the appropriate symmetry. The approach used provides explicit equivariance at each stage of the derivation of the relationship. | The topic within which this work was carried out is related to the establishment of relationships between various methods of machine learning (ML). The ultimate goal of establishing such interrelations is to achieve a better theoretical understanding of these methods and their improvement. In particular, a correspondence has recently been established between the appropriate asymptotics of deep neural networks (DNNs), including convolutional ones (CNNs), and the ML method based on Gaussian processes. Since Gaussian processes are mathematically equivalent to free (Euclidean) quantum field theory (QFT), one of the intriguing consequences of these relationships is the potential for using a broad range of QFT methods for analyzing DNNs. There are evidences (including experimental) that non-asymptotic (that is, implementable in practice) DNNs correspond to QFT with interactions. An important feature of convolutional networks is their equivariance (consistency) with respect to the symmetry transformations of the input data. In this work, we establish a relationship between the many-channel limit of equivariant CNNs and the corresponding equivariant Gaussian process (GP), and hence the QFT with the appropriate symmetry. The approach used provides explicit equivariance at each stage of the derivation of the relationship. | ||
+ | |||
+ | ===== Modeling images of proton events for the TAIGA project using a generative adversarial network: features of the network architecture and the learning process ===== | ||
+ | |||
+ | **Ju.Dubenskaya**, | ||
+ | A.P.Kryukov, | ||
+ | |||
+ | //Short presentation (15 min)// | ||
+ | |||
+ | High-energy particles interacting with the Earth atmosphere give rise to extensive air showers emitting Cherenkov light. This light can be detected on the ground by imaging atmospheric Cherenkov telescopes (IACTs). One of the main problems solved during primary processing of experimental data is the separation of signal events (gamma-quanta) against the hadronic background, the bulk of which is made up of proton events. To ensure correct gamma event/ | ||
===== 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 ===== | ||
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- | **Elizaveta Gres, ISU, Irkutsk** \\ | + | **Elizaveta Gres, ISU, Irkutsk, Russia** \\ |
- | Alexander Kryukov, SINP MSU, Moscow | + | Alexander Kryukov, SINP MSU, Moscow, Russia |
//Short presentation (15 min)// | //Short presentation (15 min)// | ||
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===== 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 ===== | ||
- | **Mikhail Krinitskiy, Shirshov Institute of Oceanology, RAS** \\ | + | **Mikhail Krinitskiy, Shirshov Institute of Oceanology, RAS, Russia** \\ |
- | Viktor Stepanenko, Research Computing Center, MSU \\ | + | Viktor Stepanenko, Research Computing Center, MSU, Russia |
- | Ruslan Chernyshev, Research Computing Center, MSU | + | Ruslan Chernyshev, Research Computing Center, MSU, Russia |
- | //Short presentation (30 min)// | + | //Long presentation (30 min)// |
The core of a land surface scheme in climate models is a solver for a nonlinear PDE system describing thermal conductance and water diffusion in soil. This system includes thermal conductivity and water diffusivity coefficients that are functions of the solution of the system, i.e., water vapor content W and soil temperature T. For the climate models to accurately represent the Earth system' | The core of a land surface scheme in climate models is a solver for a nonlinear PDE system describing thermal conductance and water diffusion in soil. This system includes thermal conductivity and water diffusivity coefficients that are functions of the solution of the system, i.e., water vapor content W and soil temperature T. For the climate models to accurately represent the Earth system' | ||
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- | Alexander Sboev, National Research Centre " | + | Alexander Sboev, National Research Centre " |
- | Ivan Moloshnikov, | + | Ivan Moloshnikov, |
- | **Alexander Naumov, National Research Centre " | + | **Alexander Naumov, National Research Centre " |
- | Anastasia Levochkina, National Research Centre " | + | Anastasia Levochkina, National Research Centre " |
//Short presentation (15 min)// | //Short presentation (15 min)// | ||
The problem of forecasting the evolution of the Covid-19 pandemic is extremely relevant because of the need for planning hospital beds demand and containment policies. Given the limitation of the available temporal datasets of the Covid-19 evolution, the complexity of machine learning algorithms to be created for pandemic time series data must not be high, and their efficiency mainly depends on the correct selection of highly-meaningful features. We view as one such feature the number of tweets where Internet users report having Covid-19. The task to extract such tweets from the internet is complicated by the lack of a Russian tweet dataset for training the machine learning models for extracting this category of tweets. In this work we present a corpus of about 10000 tweets labelled with the following classes: the Ill class when the authors declare that they are ill; the Recovered class when the authors declare that they have been ill and recovered, and the Others class of all other cases. Using this corpus, a Data-Driven model based on XLM-RoBERTa language model has been trained. It demonstrates the F1-macro accuracy of 0.85 for binary task (class 1 – Ill / Recovered, class 2 – Others), and 0.60 for the five-class task (Ill with high/low confidence, Recovered with high/low confidence, Others). These results outperform the RuDR-BERT model by 5 to 7%. The XLM-RoBERTa model thus obtained has been applied to the binary classification task for the unlabeled data of 486 000 tweets. Based on the model’s predictions, | The problem of forecasting the evolution of the Covid-19 pandemic is extremely relevant because of the need for planning hospital beds demand and containment policies. Given the limitation of the available temporal datasets of the Covid-19 evolution, the complexity of machine learning algorithms to be created for pandemic time series data must not be high, and their efficiency mainly depends on the correct selection of highly-meaningful features. We view as one such feature the number of tweets where Internet users report having Covid-19. The task to extract such tweets from the internet is complicated by the lack of a Russian tweet dataset for training the machine learning models for extracting this category of tweets. In this work we present a corpus of about 10000 tweets labelled with the following classes: the Ill class when the authors declare that they are ill; the Recovered class when the authors declare that they have been ill and recovered, and the Others class of all other cases. Using this corpus, a Data-Driven model based on XLM-RoBERTa language model has been trained. It demonstrates the F1-macro accuracy of 0.85 for binary task (class 1 – Ill / Recovered, class 2 – Others), and 0.60 for the five-class task (Ill with high/low confidence, Recovered with high/low confidence, Others). These results outperform the RuDR-BERT model by 5 to 7%. The XLM-RoBERTa model thus obtained has been applied to the binary classification task for the unlabeled data of 486 000 tweets. Based on the model’s predictions, | ||
+ | |||
+ | ===== Performance of convolutional neural networks processing simulated IACT images in the TAIGA experiment ===== | ||
+ | |||
+ | **Stanislav Polyakov**, SINP MSU, Russia \\ | ||
+ | Alexander Kryukov, | ||
+ | Evgeny Postnikov SINP MSU, Russia | ||
+ | |||
+ | //Short presentation (15 min)// | ||
+ | |||
+ | Extensive air showers created by high-energy particles interacting | ||
+ | with the Earth atmosphere can be detected using imaging atmospheric | ||
+ | Cherenkov telescopes (IACTs). The IACT images can be analyzed to | ||
+ | distinguish between the events caused by gamma rays and by hadrons and | ||
+ | to infer the parameters of the event such as the energy of the primary | ||
+ | particle. We use convolutional neural networks (CNNs) to analyze Monte | ||
+ | Carlo-simulated images of the telescopes of the TAIGA experiment. The | ||
+ | analysis includes selection of the images corresponding to the showers | ||
+ | caused by gamma rays and estimates of the energy of the gamma rays. We | ||
+ | compare performance of the CNNs using images from a single telescope | ||
+ | and the CNNs using images from two telescopes as inputs. \\ | ||
+ | //Keywords: deep learning; convolutional neural networks; gamma astronomy; | ||
+ | extensive air shower; IACT; stereoscopic mode; TAIGA// | ||
===== 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 ===== | ||
- | A.G. Sboev, NRC «Kurchatov Institute», | + | A.G. Sboev, NRC «Kurchatov Institute», |
- | **A.A. Selivanov, NRC «Kurchatov Institute»** \\ | + | **A.A. Selivanov, NRC «Kurchatov Institute», Russia** \\ |
- | R.B. Rybka, NRC «Kurchatov Institute»; I.A. Moloshnikov NRC «Kurchatov Institute» | + | R.B. Rybka, NRC «Kurchatov Institute», Russia \\ |
+ | I.A. Moloshnikov NRC «Kurchatov Institute», Russia | ||
//Short presentation (15 min)// | //Short presentation (15 min)// | ||
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===== Using modern machine learning methods on KASCADE data for science and education ===== | ===== Using modern machine learning methods on KASCADE data for science and education ===== | ||
- | **Victoria Tokareva, IAP KIT** | + | **Victoria Tokareva, IAP KIT, Germany** |
//Short presentation (15 min)// | //Short presentation (15 min)// | ||
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===== Gamma/ | ===== Gamma/ | ||
- | **Vasyutina M.R., Moscow State University. Physical Department** \\ | + | **Vasyutina M.R., Moscow State University. Physical Department, Russia** \\ |
- | Sveshnikova L.G., Moscow State University. Skobeltsyn Institute of Nuclear Research. | + | Sveshnikova L.G., SINP MSU, Russia. |
//Short presentation (15 min)// | //Short presentation (15 min)// | ||
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===== Using convolutional neural network for analysis of HiSCORE events ===== | ===== Using convolutional neural network for analysis of HiSCORE events ===== | ||
- | **Vlaskina Anna, Physics Department, MSU** \\ | + | **Vlaskina Anna, Physics Department, MSU, Russia** \\ |
- | A. Kryukov, SINP MSU | + | A. Kryukov, SINP MSU, Russia |
//Short presentation (15 min)// | //Short presentation (15 min)// | ||
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===== 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 ===== | ||
+ | **A.Zaborenko, | ||
Lev Dudko, SINP MSU,Russia \\ | Lev Dudko, SINP MSU,Russia \\ | ||
Petr Volkov, SINP MSU,Russia \\ | Petr Volkov, SINP MSU,Russia \\ |
dlcp21/abstracts.txt · Last modified: 22/06/2021 19:54 by admin