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Zsuzsa Márka
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2020 – today
- 2023
- [i7]Jingkai Yan, Shiyu Wang, Xinyu Rain Wei, Jimmy Wang, Zsuzsanna Márka, Szabolcs Márka, John Wright:
TpopT: Efficient Trainable Template Optimization on Low-Dimensional Manifolds. CoRR abs/2310.10039 (2023) - 2022
- [i6]Robert E. Colgan, Jingkai Yan, Zsuzsa Márka, Imre Bartos, Szabolcs Márka, John N. Wright:
Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets. CoRR abs/2202.13486 (2022) - [i5]Robert E. Colgan, Zsuzsa Márka, Jingkai Yan, Imre Bartos, John N. Wright, Szabolcs Márka:
Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning. CoRR abs/2203.05086 (2022) - [i4]Doga Veske, Cenk Tüysüz
, Mirko Amico, Nicholas T. Bronn, Olivia T. Lanes, Zsuzsa Márka, Szabolcs Márka:
Quantum encoding is suitable for matched filtering. CoRR abs/2204.04159 (2022) - [i3]Jingkai Yan, Robert E. Colgan, John Wright, Zsuzsa Márka, Imre Bartos, Szabolcs Márka:
Boosting the Efficiency of Parametric Detection with Hierarchical Neural Networks. CoRR abs/2207.11583 (2022) - 2021
- [j1]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, Leïla Haegel, Alberto Iess
, David Keitel
, Zsuzsa Márka, Szabolcs Márka, Filip Morawski
, Tri Nguyen, Rich Ormiston, Michael Pürrer, Massimiliano Razzano
, Kai Staats, Gabriele Vajente, Daniel Williams
:
Enhancing gravitational-wave science with machine learning. Mach. Learn. Sci. Technol. 2(1): 11002 (2021) - [i2]Jingkai Yan, Mariam Avagyan, Robert E. Colgan, Doga Veske, Imre Bartos, John N. Wright, Zsuzsa Márka, Szabolcs Márka:
Generalized Approach to Matched Filtering using Neural Networks. CoRR abs/2104.03961 (2021)
2010 – 2019
- 2019
- [i1]Eliu A. Huerta, Gabrielle Allen, Igor Andreoni, Javier Mauricio Antelis, Etienne Bachelet, G. Bruce Berriman, Federica B. Bianco, Rahul Biswas, Matias Carrasco Kind, Kyle Chard, Minsik Cho, Philip S. Cowperthwaite, Zachariah B. Etienne, Maya Fishbach, Francisco Förster
, Daniel George, Tom Gibbs, Matthew J. Graham, William Gropp, Robert A. Gruendl, Anushri Gupta, Roland Haas
, Sarah Habib, Elise Jennings, Margaret W. G. Johnson, Erik Katsavounidis, Daniel S. Katz, Asad Khan, Volodymyr V. Kindratenko, William T. C. Kramer, Xin Liu, Ashish Mahabal, Zsuzsa Márka, Kenton McHenry, Jonah Miller, Claudia Moreno, Mark S. Neubauer, Steve Oberlin, Alexander R. Olivas, Donald Petravick, Adam Rebei, Shawn Rosofsky, Milton Ruiz, Aaron Saxton, Bernard F. Schutz, Alexander G. Schwing, Ed Seidel, Stuart L. Shapiro, Hongyu Shen, Yue Shen, Leo Singer, Brigitta M. Sipocz
, Lunan Sun, John Towns, Antonios Tsokaros, Wei Wei, Jack Wells, Timothy J. Williams, Jinjun Xiong, Zhizhen Zhao:
Enabling real-time multi-messenger astrophysics discoveries with deep learning. CoRR abs/1911.11779 (2019)
Coauthor Index
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