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Gravitational-wave parameter inference using Deep Learning.

, , , , , , , и . CBMI, стр. 1-6. IEEE, (2021)

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António Marques

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Gravitational-wave parameter inference using Deep Learning., , , , , , , и . CBMI, стр. 1-6. IEEE, (2021)Comparison of neural network architectures for feature extraction from binary black hole merger waveforms., , , , , и . Mach. Learn. Sci. Technol., 5 (1): 15036 (марта 2024)Removing Inefficiencies from Scientific Code: The Study of the Higgs Boson Couplings to Top Quarks., , и . ICCSA (4), том 8582 из Lecture Notes in Computer Science, стр. 576-591. Springer, (2014)Machine-Learning Love: classifying the equation of state of neutron stars with Transformers., , , , , , и . CoRR, (2022)Identification of Binary Neutron Star Mergers in Gravitational-Wave Data Using YOLO One-Shot Object Detection., , , , , , и . CoRR, (2022)Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams., , , , , и . CoRR, (2023)Tuning pipelined scientific data analyses for efficient multicore execution., , и . HPCS, стр. 751-758. IEEE, (2016)External validation of the unified model of information systems continuance (UMISC): An international comparison., , , , , и . Int. J. Medical Informatics, (2020)Active Participation and Engagement of Residents in Clinical Informatics., , и . Appl. Clin. Inform., 10 (01): 38-39 (2019)