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dlcp21:abstracts [22/06/2021 16:13] – [The Russian language corpus and a neural network to analyse Internet tweet reports about Covid-19] admindlcp21:abstracts [22/06/2021 19:54] (current) – [Equivariant Gaussian Processes as Limiting Convolutional Networks with Infinite Number of Channels] admin
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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**, SINP MSU, Russia \\ 
 +A.P.Kryukov,  SINP MSU, Russia
 + 
 +//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/proton event separation under real conditions, a large amount of experimental data, including model data, is required. Thus, although proton events are considered as background, their images are also necessary for accurate registration of gamma quanta. We applied a machine learning method - generative adversarial networks to generate images of proton events for the TAIGA project. This approach allowed us to significantly increase the speed of image generation. At the same time testing the results using third-party software showed that over 90% of the generated images were correct. In this article we provide an example of a GAN architecture suitable for generating images of proton events similar to those obtained from IACTs of the TAIGA project. The features of the training process are also discussed, including the number of learning epochs and selecting appropriate network parameters.
 ===== 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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 Alexander Kryukov,  SINP MSU, Russia \\  Alexander Kryukov,  SINP MSU, Russia \\ 
 Evgeny Postnikov SINP MSU, Russia Evgeny Postnikov SINP MSU, Russia
 +
 +//Short presentation (15 min)//
  
 Extensive air showers created by high-energy particles interacting Extensive air showers created by high-energy particles interacting
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 caused by gamma rays and estimates of the energy of the gamma rays. We 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 compare performance of the CNNs using images from a single telescope
-and the CNNs using images from two telescopes as inputs. +and the CNNs using images from two telescopes as inputs. \\ 
 //Keywords: deep learning; convolutional neural networks; gamma astronomy; //Keywords: deep learning; convolutional neural networks; gamma astronomy;
 extensive air shower; IACT; stereoscopic mode; TAIGA// extensive air shower; IACT; stereoscopic mode; TAIGA//
dlcp21/abstracts.1624367621.txt.gz · Last modified: 22/06/2021 16:13 by admin