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检索条件"机构=Data Analysis and Machine Learning"
199 条 记 录,以下是101-110 订阅
排序:
Gravitational-Wave Parameter Estimation in non-Gaussian noise using Score-Based Likelihood Characterization
arXiv
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arXiv 2024年
作者: Legin, Ronan Isi, Maximiliano Wong, Kaze W.K. Hezaveh, Yashar Perreault-Levasseur, Laurence Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning MontréalQC Canada Department of Physics Université de Montréal MontréalQC Canada Mila - Quebec Artificial Intelligence Institute MontréalQC Canada Center for Computational Astrophysics Flatiron Institute New YorkNY United States Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD United States Trottier Space Institute MontréalQC Canada Perimeter Institute for Theoretical Physics WaterlooON Canada
Gravitational-wave (GW) parameter estimation typically assumes that instrumental noise is Gaussian and stationary. Obvious departures from this idealization are typically handled on a case-by-case basis, e.g., through... 详细信息
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Active learning meets fractal decision boundaries: a cautionary tale from the Sitnikov three-body problem
arXiv
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arXiv 2023年
作者: Payot, Nicolas Pasquato, Mario Trani, Alessandro Alberto Hezaveh, Yashar Perreault-Levasseur, Laurence Département de Physique Université de Montréal Mila Quebec Artificial Intelligence Institute Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Canada Dipartimento di Fisica e Astronomia Università di Padova Vicolo dell’Osservatorio 5 PadovaI-35122 Italy Niels Bohr Institute Copenhagen Denmark Research Center for the Early Universe School of Science The University of Tokyo Tokyo Japan Okinawa Institute of Science and Technology Okinawa Japan
Chaotic systems such as the gravitational N-body problem are ubiquitous in astronomy. machine learning (ML) is increasingly deployed to predict the evolution of such systems, e.g. with the goal of speeding up simulati... 详细信息
来源: 评论
Autoencoder-based Ultrasonic NDT of Adhesive Bonds
Autoencoder-based Ultrasonic NDT of Adhesive Bonds
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IEEE SENSORS
作者: Ivan Kraljevski Frank Duckhorn Martin Barth Constanze Tschoepe Frank Schubert Matthias Wolff Cognitive Material Diagnostics Fraunhofer IKTS Cottbus Germany Machine Learning and Data Analysis Fraunhofer IKTS Dresden Germany Ultrasonic Sensors and Methods Fraunhofer IKTS Dresden Germany Chair of Communications Engineering BTU Cottbus-Senftenberg Cottbus Germany
We present an approach for ultrasonic non-destructive testing of adhesive bonding employing unsupervised machine learning with *** models are trained exclusively on the features derived from pulse-echo ultrasonic sign... 详细信息
来源: 评论
learning an Effective Evolution Equation for Particle-Mesh Simulations Across Cosmologies
arXiv
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arXiv 2023年
作者: Payot, Nicolas Lemos, Pablo Perreault-Levasseur, Laurence Cuesta-Lazaro, Carolina Modi, Chirag Hezaveh, Yashar Département de Physique Université de Montréal Mila Quebec Artificial Intelligence Institute Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Canada Center for Computational Astrophysics Flatiron Institute NY United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions Massachusetts Institute of Technology CambridgeMA United States Center for Astrophysics Harvard & Smithsonian CambridgeMA United States Center for Computational Mathematics Flatiron Institute NY United States
Particle-mesh simulations trade small-scale accuracy for speed compared to traditional, computationally expensive N-body codes in cosmological simulations. In this work, we show how a data-driven model could be used t... 详细信息
来源: 评论
Overview of Modern Forecasting Methods and Models
Overview of Modern Forecasting Methods and Models
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International Conference on Management of Large-Scale System Development (MLSD)
作者: Vera Ivanyuk Anatoliy Tsvirkun Anna Sunchalina Tatiana Goroshnikova Andrey Sunchalin Galina Zholobova Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia Department of Higher Mathematics Bauman Moscow State Technical University Moscow Russia Laboratory of Management of the Development of Large-Scale Systems V.A.Trapeznikov Institute of Control Sciences of RAS Moscow Russia Faculty of International Economic Relations Financial University Under the Government of the Russian Federation Moscow Russia Department of Mathematics Financial University Under the Government of the Russian Federation Moscow Russia
The development of accurate forecasting models for financial time series is an essential area of research in finance and economics. The article describes modern models and forecasting methods.
来源: 评论
Neural networks and their application in forecasting problems  23
Neural networks and their application in forecasting problem...
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23rd International Conference on Soft Computing and Measurement, SCM 2020
作者: Ivanyuk, V.A. Pashchenko, F.F. Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation 49 Leningradsky Prospekt Moscow125993 Russia Department of Higher Mathematics Bauman Moscow State Technical University Moscow Russia V.A. Trapeznikov Institute of Control Sciences of RAS Moscow Russia
The report describes popular machine learning methods and applications of neural networks. It reveals methods of training neural networks and offers a method of forecasting based on neural networks for modelling finan... 详细信息
来源: 评论
Ensemble forecasting method  23
Ensemble forecasting method
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23rd International Conference on Soft Computing and Measurement, SCM 2020
作者: Ivanyuk, V.A. Tsvirkun, A.D. Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation 49 Leningradsky Prospekt Moscow125993 Russia Department of Higher Mathematics Bauman Moscow State Technical University Moscow Russia V.A. Trapeznikov Institute of Control Sciences of RAS Moscow Russia
The purpose of this article is to analyze the time series based on aggregate forecasting methods. Forecasting time series comprises an important scientific and technical task which is relevant in various sectors of ec... 详细信息
来源: 评论
Forecasting the dynamics of financial time series based on neural networks  23
Forecasting the dynamics of financial time series based on n...
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23rd International Conference on Soft Computing and Measurement, SCM 2020
作者: Ivanyuk, V.A. Abdikeev, N.M. Tsvirkun, A.D. Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation 49 Leningradsky Prospekt Moscow125993 Russia Department of Higher Mathematics Bauman Moscow State Technical University Moscow Russia V.A. Trapeznikov Institute of Control Sciences of RAS Moscow Russia
Forecasting is one of the high-demand data mining problems, but also a very difficult one. The difficulties of forecasting are associated with insufficient quality and quantity of input data, the changes in the enviro... 详细信息
来源: 评论
The Intrinsic Flattening of Extragalactic Stellar Disks
arXiv
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arXiv 2024年
作者: Favaro, Jeremy Courteau, Stephane Comerón, Sébastien Stone, Connor Department of Physics Engineering Physics & Astronomy Queen’s University KingstonONK7L 3N6 Canada Departamento de Astrofísica Universidad de La Laguna Tenerife La Laguna38200 Spain Instituto de Astrofísica de Canarias Tenerife La Laguna38205 Spain Department of Physics Université de Montréal MontréalQC Canada Mila - Québec Artificial Intelligence Institute MontréalQC Canada Ciela - Montréal Institute for Astrophysical Data Analysis and Machine Learning MontréalQC Canada
Highly inclined (edge-on) disk galaxies offer the unique perspective to constrain their intrinsic flattening, c/a, where c and a are respectively the vertical and long radial axes of the disk measured at suitable stel... 详细信息
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Hierarchical Bayesian approach for adaptive integration of Bragg peaks in time-of-flight neutron scattering data
arXiv
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arXiv 2024年
作者: Reshniak, Viktor Wang, Xiaoping Zhang, Guannan Liu, Siyan Yin, Junqi Data Analysis and Machine Learning Group Oak Ridge National Laboratory 1 Bethel Valley Rd Oak RidgeTN37830 United States Single Crystal Diffraction Oak Ridge National Laboratory 1 Bethel Valley Rd Oak RidgeTN37830 United States Computational Earth Sciences Group Oak Ridge National Laboratory 1 Bethel Valley Rd Oak RidgeTN37830 United States Analytics and AI Methods at Scale Oak Ridge National Laboratory 1 Bethel Valley Rd Oak RidgeTN37830 United States
The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) operates in the event mode. Time-of-flight (TOF) information about each detected neutron is collected separately and saved as a descriptive e... 详细信息
来源: 评论