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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
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
Enabling real-time multi-messenger astrophysics discoveries with deep learning. CoRR abs/1911.11779 (2019)
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last updated on 2024-10-07 21:09 CEST by the dblp team
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