dlcp21:abstracts
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dlcp21:abstracts [20/06/2021 00:51] – created kryukov | 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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- | ====== | + | ====== |
+ | |||
+ | //Draft// | ||
+ | |||
+ | ===== The technology of long-term forecasting of water inflow into reservoirs using a multi-parameter neural network ===== | ||
+ | |||
+ | |||
+ | **N.V. Abasov, Melentiev Energy Systems Institute SB RAS, Russia** \\ | ||
+ | E.N. Osipchuk, Melentiev Energy Systems Institute SB RAS, Russia \\ | ||
+ | V.M. Berdnikov, Melentiev Energy Systems Institute SB RAS, Russia | ||
+ | |||
+ | //Short presentation (15 min)// | ||
+ | |||
+ | The technology of long-term forecasting of water inflow into reservoirs from a month to several years is considered. Using created by the authors a multi-parameter neural network (MNN), a forecasting technology has been developed with stages: 1) search for potential predictors in the form of areas correlated with water inflow by the vorticity indices of the selected atmospheric layer; 2) neural network model is synthesized with different parameters (prediction accuracy in the form of division into a finite number of intervals, a list of predictors, the number of hidden layers, the number of neurons of different types in each layer). In most cases, for a different set of predictors learning occurs successfully on the training samples. Significant errors occur on verification samples. To reduce them an algorithm has been developed for automatic search and selection of predictors, varying the structural parameters of MNN with the selection of models that form the minimum errors on the verification samples. Models with a maximum deviation error of no more than one interval are selected. The final forecast decision is determined based on the creation of probability distributions over a set of selected models. In further studies, it is proposed to use indices that take into account the power of cosmic rays, solar activity, the influence of various planets, and other factors that can exclude or reduce errors in the verification samples. | ||
+ | |||
+ | |||
+ | ===== Legacy of Tunka-Rex software and data. ===== | ||
+ | |||
+ | **Pavel Bezyazeekov, | ||
+ | |||
+ | Tunka-Rex is a digital antenna array for measuring the radio emission from air-showers, | ||
+ | |||
+ | //Short presentation (15 min)// | ||
+ | |||
+ | ===== Equivariant Gaussian Processes as Limiting Convolutional Networks with Infinite Number of Channels ===== | ||
+ | |||
+ | **A.Demichev, | ||
+ | |||
+ | //Short presentation (15 min)// | ||
+ | |||
+ | 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 ===== | ||
+ | |||
+ | **A.O.Efitorov, | ||
+ | S.A.Burikov, | ||
+ | T.A.Dolenko, | ||
+ | K.A.Laptinskiy, | ||
+ | S.A.Dolenko, | ||
+ | |||
+ | //Short presentation (15 min)// | ||
+ | |||
+ | The report considers the approach of improving the results of solving the inverse problem of spectroscopy of water-ethanol solutions by generating an additional array of patterns by a generative adversarial neural network (GAN). To solve this problem, 40710 Raman spectra (low-frequency region + region of the valence band of water) of ethanol solutions containing impurities (methanol, ethyl acetate, fusel oil) in various concentrations or without impurities were recorded in laboratory conditions. More than 8 thousand examples were extracted from the dataset as a test set, which was used only to assess the performance of the trained classifier network. The considered problem was that of detecting presence of each of the three possible impurities. In order to increase the number of patterns, a conditioned GAN (1D deep convolutional network) was trained, the “conditional” parameter was a vector with binary encoding of the presence of different components in the water-ethanol solution. Due to the significant differences in the structure of the spectra of the low-frequency spectral region (a number of narrow high-intensity peaks) and the region of the valence band of water (no narrow peaks, a smooth shape of the valence band), for each spectral region its own generator-discriminator pair was trained. After training cGAN, more than 40 thousand examples were generated. Then an additional neural network was trained to solve the classification problem. Various combinations of patterns in the classification training set were considered: real spectra only, generated spectra only, merged (generated + real) dataset. A comparative analysis of the results of these approaches on a test set of real spectra demonstrated that the joint use of generated and real data increased the accuracy of solving the classification problem. The study was supported by the Russian Foundation for Basic Research, project no. 19-01-00738.No presentation | ||
+ | |||
+ | ===== A convolutional hierarchical neural network classifier ===== | ||
+ | |||
+ | **I.M. Gadzhiev, SINP MSU, Russia** \\ | ||
+ | S.A. Dolenko, SINP MSU, Russia | ||
+ | |||
+ | //Short presentation (15 min)// | ||
+ | |||
+ | The report presents an algorithm for constructing a convolutional hierarchical neural network classifier, which is a modification of the algorithm for constructing hierarchical neural network classifiers suggested before. The original algorithm was designed to exploit intrinsic class hierarchy to build a class tree with a neural network in each node classifying groups of initial classes (in a non-terminal node) or a subset of original classes (in a terminal node). The convolutional modification utilizes convolutional neural networks instead of regular fully connected networks in order to apply the model to image classification tasks. Use of class hierarchy for image classification should reduce the number of adjusted neural network parameters compared to deep convolutional neural networks, and therefore it should reduce training and inference time. In this context the algorithm may be compared with some pruning techniques. The convolutional hierarchical neural network classifier inherits some hyperparameters of a conventional hierarchical neural network classifier, like the activation threshold and the threshold by the share of voting patterns. The goal of this study was to explore different strategies of choosing these hyperparameters. To test these strategies, we used the CIFAR-10 dataset. Also, for demonstration purposes we apply the convolutional hierarchical neural network classifier to the CIFAR-100 dataset. This study has been funded by the SINP MSU state budget topic 6.1 (01201255512). | ||
+ | |||
+ | ===== The preliminary results on analysis of TAIGA-IACT images using Convolutional Neural Networks ===== | ||
+ | |||
+ | |||
+ | **Elizaveta Gres, ISU, Irkutsk, Russia** \\ | ||
+ | Alexander Kryukov, SINP MSU, Moscow, Russia | ||
+ | |||
+ | //Short presentation (15 min)// | ||
+ | |||
+ | The imaging Cherenkov telescopes TAIGA-IACT, located in The Tunka valley of the republic Buryatia, accumulate a lot of data in a short period of time which must be qualitatively and quickly analyzed. One of the methods of such analysis is the machine learning, which has proven its effectiveness in many technological and scientific fields in recent years. The aim of the work is to study the possibility of the machine learning application to solve the tasks set for TAIGA-IACT. In the work the method of Convolutional Neural Networks was applied to process and analyze Monte-Carlo events simulated with CORSIKA. Also various CNN architectures for the processing were considered. It has been demonstrated that this method gives good results of the determination the type of primary particles of Extensive Air Shower (EAS) and the recovery of gamma-rays energy. The results are significantly improved in the case of stereoscopic observations. | ||
+ | |||
+ | |||
+ | ===== Neural network solution of inverse problems of geological prospecting with discrete output ===== | ||
+ | |||
+ | **Igor Isaev, SINP MSU, Moscow, Russia, Kotelnikov Institute of Radio Engineering and Electronics, | ||
+ | Ivan Obornev, SINP MSU, Moscow, Russia \\ | ||
+ | Eugeny Obornev, S.Ordjonikidze Russian State Geological Prospecting University, Moscow, Russia \\ | ||
+ | Eugeny Rodionov, S.Ordjonikidze Russian State Geological Prospecting University, Moscow, Russia \\ | ||
+ | Mikhail Shimelevich, | ||
+ | Sergey Dolenko, SINP MSU, Moscow, Russia | ||
+ | |||
+ | //Short presentation (15 min)// | ||
+ | |||
+ | The inverse problems of exploration geophysics are to reconstruct the spatial distribution of the properties of the medium in the Earth' | ||
+ | |||
+ | ===== Graph Neural Networks and application for Cosmic-Ray Analysis ===== | ||
+ | |||
+ | **Paras Koundal; IAP , KIT Karlsruhe, Germany ** | ||
+ | |||
+ | //Long presentation (30 min)// | ||
+ | |||
+ | Deep Learning has emerged as one of the most promising areas of computational research for pattern learning, inference drawing, and decision-making, | ||
+ | |||
+ | ===== Identifying partial differential equations of land surface schemes in INM climate models with neural networks ===== | ||
+ | |||
+ | **Mikhail Krinitskiy, Shirshov Institute of Oceanology, RAS, Russia** \\ | ||
+ | Viktor Stepanenko, Research Computing Center, MSU, Russia \\ | ||
+ | Ruslan Chernyshev, Research Computing Center, MSU, Russia | ||
+ | |||
+ | //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' | ||
+ | |||
+ | ===== TAIGA: status, results and perspectives ===== | ||
+ | |||
+ | |||
+ | **L.Kuzmichev (SINP MSU) for the TAIGA collaboration** | ||
+ | |||
+ | //Invited presentation (45 min)// | ||
+ | |||
+ | TAIGA (Tunka Advanced Instrument for cosmic ray physics and Gamma Astronomy) Astrophysical complex, located in the Tunka Valley, about 50 km from Lake Baikal, | ||
+ | |||
+ | ===== The Russian language corpus and a neural network to analyse Internet tweet reports about Covid-19 ===== | ||
+ | |||
+ | |||
+ | Alexander Sboev, National Research Centre " | ||
+ | Ivan Moloshnikov, | ||
+ | **Alexander Naumov, National Research Centre " | ||
+ | Anastasia Levochkina, National Research Centre " | ||
+ | |||
+ | //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, | ||
+ | |||
+ | ===== 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 ===== | ||
+ | |||
+ | A.G. Sboev, NRC «Kurchatov Institute», | ||
+ | **A.A. Selivanov, NRC «Kurchatov Institute», | ||
+ | R.B. Rybka, NRC «Kurchatov Institute», | ||
+ | I.A. Moloshnikov NRC «Kurchatov Institute», | ||
+ | |||
+ | //Short presentation (15 min)// | ||
+ | |||
+ | The considered problem is automatic recognition of the relations between mentions of adverse drug reactions and medications in russian online drug reviews. This task solution is useful for pharmacovigilance and reprofiling of medicines. This problem hasn’t been studied for the Russian language, due to the lack of corpora with relation labeling in Russian. Current research is based on a developed dataset with labeling of relations between entities from the Russian Drug Review Corpus of Russian Internet reviews on medications. Computational experiments were carried out on developed corpora using classical machine learning methods, as well as more advanced BERT topology model — RuDR-BERT. The classical machine learning methods were: support vector machine, logistic regression, Naive Bayes classifier and gradient boosting. In frame of these methods, concatenation of entity vectors obtained using TF-IDF of characters n-gram was used as a vector data representation for words, also the following hyperparameters of these method were selected based on a set of experiments: | ||
+ | |||
+ | ===== Using modern machine learning methods on KASCADE data for science and education ===== | ||
+ | |||
+ | **Victoria Tokareva, IAP KIT, Germany** | ||
+ | |||
+ | //Short presentation (15 min)// | ||
+ | |||
+ | Modern astroparticle physics makes wide use of machine learning methods in such problems as noise separation, image recognition, | ||
+ | |||
+ | |||
+ | ===== Gamma/ | ||
+ | |||
+ | **Vasyutina M.R., Moscow State University. Physical Department, Russia** \\ | ||
+ | Sveshnikova L.G., SINP MSU, Russia. | ||
+ | |||
+ | //Short presentation (15 min)// | ||
+ | |||
+ | In this report we present adaptation of the machine learning algorithm Random Forest (RF) to the gamma/ | ||
+ | |||
+ | ===== Using convolutional neural network for analysis of HiSCORE events ===== | ||
+ | |||
+ | **Vlaskina Anna, Physics Department, MSU, Russia** \\ | ||
+ | A. Kryukov, SINP MSU, Russia | ||
+ | |||
+ | //Short presentation (15 min)// | ||
+ | |||
+ | Project TAIGA is a hybrid observatory for gamma-ray astronomy at high energies in range from 10 TeV to several EeV. Project consists of instruments such as TAIGA-IACT, TAIGA-HISCORE and others. TAIGA-HISCORE, | ||
+ | |||
+ | ===== Application of deep learning technique to an analysis of hard scattering processes at colliders ===== | ||
+ | |||
+ | **A.Zaborenko, | ||
+ | Lev Dudko, SINP MSU,Russia \\ | ||
+ | Petr Volkov, SINP MSU,Russia \\ | ||
+ | G. Vorotnikov, SINP MSU, | ||
+ | |||
+ | //Short presentation (15 min)// | ||
+ | |||
+ | Deep neural networks have rightfully won the place of one of the most accurate analysis tools in high energy physics. In this talk we will cover several methods of improving the performance of a neural network in a classification task in an instance of top quark analysis. The approaches and recommendations will cover hyperparameter tuning, boosting on errors and AutoML algorithms applied to collider physics. | ||
- | //Will be soon// |
dlcp21/abstracts.1624139488.txt.gz · Last modified: 20/06/2021 00:51 by kryukov