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Cyber infrastructure will be a critical consideration in the development of next generation gravitational-wave detectors.
Backpropagation applied to handwritten zip code recognition
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1989
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Handwritten digit recognition with a back-propagation network
Le Cun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1990
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Lstm can solve hard long time lag problems
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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A coherent method for detection of gravitational wave bursts
S. Klimenko, I. Yakushin, A. Mercer, and G. Mitselmakher · 2008
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Method for all-sky searches of continuous gravitational wave signals using the frequency-hough transform
Pia Astone, Alberto Colla, Sabrina D’Antonio, Sergio Frasca, and Cristiano Palomba · 2014
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Implementing a search for aligned-spin neutron star-black hole systems with advanced ground based gravitational wave detectors
Tito Dal Canton, Alexander H. Nitz, Andrew P. Lundgren, Alex B. Nielsen, Duncan A. Brown, Thomas Dent, Ian W. Harry, Badri Krishnan, Andrew J. Miller, Karl Wette, Karsten Wiesner, and Joshua L. Willis · 2014
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The PyCBC search for gravitational waves from compact binary coalescence
Samantha A. Usman, Alexander H. Nitz, Ian W. Harry, Christopher M. Biwer, Duncan A. Brown, Miriam Cabero, Collin D. Capano, Tito Dal Canton, Thomas Dent, Stephen Fairhurst, Marcel S. Kehl, Drew Keppel, Badri Krishnan, Amber Lenon, Andrew Lundgren, Alex B. Nielsen, Larne P. Pekowsky, Harald P. Pfeiffer, Peter R. Saulson, Matthew West, and Joshua L. Willis · 2016
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Data access for LIGO on the OSG
Derek Weitzel, Brian Bockelman, Duncan A. Brown, Peter Couvares, Frank Würthwein, and Edgar Fajardo Hernandez · 2017
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Acceleration of low-latency gravitational wave searches using Maxwell-microarchitecture GPUs
Xiangyu Guo, Qi Chu, Shin Kee Chung, Zhihui Du, and Linqing Wen · 2017
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Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science
M. Zevin, S. Coughlin, S. Bahaadini, E. Besler, N. Rohani, S. Allen, M. Cabero, K. Crowston, A. K. Katsaggelos, S. L. Larson, T. K. Lee, C. Lintott, T. B. Littenberg, A. Lundgren, C. Østerlund, J. R. Smith, L. Trouille, and V. Kalogera · 2017
Cited alongside, same era.
Deep neural networks to enable real-time multimessenger astrophysics
Daniel George and E. A. Huerta · 2018
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Deep Learning for real-time gravitational wave detection and parameter estimation: Results with Advanced LIGO data
Daniel George and E. A. Huerta · 2018
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Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy
Hunter Gabbard, Michael Williams, Fergus Hayes, and Chris Messenger · 2018
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Classification and unsupervised clustering of LIGO data with Deep Transfer Learning
Daniel George, Hongyu Shen, and E. A. Huerta · 2018
Cited alongside, same era.
Hunter Gabbard, Chris Messenger, Ik Siong Heng, Francesco Tonolini, and Roderick Murray-Smith · 2019
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The Next Generation Global Gravitational Wave Observatory: The Science Book, 2020
Sathyaprakash, B. and Kalogera, V. and the GWIC 3G Science Case team · 2020
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Virgo and gravitational waves computing in europe
Stefano Bagnasco · 2020
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Trends in computing technologies and markets
Helge Meinhard, Bernd Panzer-Steindel, Servesh Muralidharan, Peter Wegner, Christopher Henry Hollowell, Michele Michelotto, Andrea Sciabá, Eric Yen, German Cancio Melia, Martin Gasthuber, Shigeki Misawa, and Edoardo Martelli · 2020
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Enhancing Gravitational-Wave Science with Machine Learning
Elena Cuoco, Jade Powell, Marco Cavaglià, Kendall Ackley, Michal Bejger, Chayan Chatterjee, Michael Coughlin, Scott Coughlin, Paul Easter, Reed Essick, Hunter Gabbard, Timothy Gebhard, Shaon Ghosh, Leila Haegel, Alberto Iess, David Keitel, Zsuzsa Marka, Szabolcs Marka, Filip Morawski, Tri Nguyen, Rich Ormiston, Michael Puerrer, Massimiliano Razzano, Kai Staats, Gabriele Vajente, and Daniel Williams · 2020
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Image-based deep learning for classification of noise transients in gravitational wave detectors
Massimiliano Razzano and Elena Cuoco · 2018
Cited alongside, same era.
Accelerating parameter inference with graphics processing units
D. Wysocki, R. O’Shaughnessy, Jacob Lange, and Yao-Lung L. Fang · 2019
Cited alongside, same era.
Surabhi Sachdev, Sarah Caudill, Heather Fong, Rico K. L. Lo, Cody Messick, Debnandini Mukherjee, Ryan Magee, Leo Tsukada, Kent Blackburn, Patrick Brady, Patrick Brockill, Kipp Cannon, Sydney J. Chamberlin, Deep Chatterjee, Jolien D. E. Creighton, Patrick Godwin, Anuradha Gupta, Chad Hanna, Shasvath Kapadia, Ryan N. Lang, Tjonnie G. F. Li, Duncan Meacher, Alexander Pace, Stephen Privitera, Laleh Sadeghian, Leslie Wade, Madeline Wade, Alan Weinstein, and Sophia Liting Xiao · 2019
Cited alongside, same era.
Convolutional neural networks: A magic bullet for gravitational-wave detection?
Timothy D. Gebhard, Niki Kilbertus, Ian Harry, and Bernhard Schölkopf · 2019
Cited alongside, same era.
Deep-learning continuous gravitational waves
Christoph Dreissigacker, Rahul Sharma, Chris Messenger, Ruining Zhao, and Reinhard Prix · 2019
Cited alongside, same era.
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Real-time detection of gravitational waves from binary neutron stars using artificial neural networks
Plamen G. Krastev · 2020
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Detection of gravitational-wave signals from binary neutron star mergers using machine learning
Marlin B. Schäfer, Frank Ohme, and Alexander H. Nitz · 2020
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Deep-learning continuous gravitational waves: Multiple detectors and realistic noise
Christoph Dreissigacker and Reinhard Prix · 2020
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Gravitational-wave parameter estimation with autoregressive neural network flows
Stephen R. Green, Christine Simpson, and Jonathan Gair · 2020
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Learning Bayesian Posteriors with Neural Networks for Gravitational-Wave Inference
Alvin J. K. Chua and Michele Vallisneri · 2020
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