Enabling real-time multi-messenger astrophysics discoveries with deep learning. - 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; et al. (CoRR, 2019)

Generalized Approach to Matched Filtering using Neural Networks. - Jingkai Yan; Mariam Avagyan; Robert E. Colgan; Doga Veske; Imre Bartos; John N. Wright; Zsuzsa Márka; Szabolcs Márka (CoRR, 2021)

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; et al. (Mach. Learn. Sci. Technol., 2021)

Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets. - Robert E. Colgan; Jingkai Yan; Zsuzsa Márka; Imre Bartos; Szabolcs Márka; John N. Wright (CoRR, 2022)

Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning. - Robert E. Colgan; Zsuzsa Márka; Jingkai Yan; Imre Bartos; John N. Wright; Szabolcs Márka (CoRR, 2022)

Quantum encoding is suitable for matched filtering. - Doga Veske; Cenk Tüysüz; Mirko Amico; Nicholas T. Bronn; Olivia T. Lanes; Zsuzsa Márka; Szabolcs Márka (CoRR, 2022)

Boosting the Efficiency of Parametric Detection with Hierarchical Neural Networks. - Jingkai Yan; Robert E. Colgan; John Wright 001; Zsuzsa Márka; Imre Bartos; Szabolcs Márka (CoRR, 2022)

TpopT: Efficient Trainable Template Optimization on Low-Dimensional Manifolds. - Jingkai Yan; Shiyu Wang; Xinyu Rain Wei; Jimmy Wang; Zsuzsanna Márka; Szabolcs Márka; John Wright 001 (CoRR, 2023)

Fast, Accurate Manifold Denoising by Tunneling Riemannian Optimization. - Shiyu Wang; Mariam Avagyan; Yihan Shen; Arnaud Lamy; Tingran Wang; Szabolcs Márka; Zsuzsa Márka; John Wright 001 (CoRR, 2025)