Sampling with prior knowledge for high-dimensional gravitational wave data analysis. - He Wang 018; Zhoujian Cao; Yue Zhou 014; Zong-Kuan Guo; Zhixiang Ren (Big Data Min. Anal., 2022)

Space-based gravitational wave signal detection and extraction with deep neural network. - Tianyu Zhao; Ruoxi Lyu; Zhixiang Ren; He Wang 018; Zhoujian Cao (CoRR, 2022)

MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge. - Marlin B. Schäfer; Ondrej Zelenka; Alexander H. Nitz; He Wang 018; Shichao Wu; Zong-Kuan Guo; Zhoujian Cao; Zhixiang Ren; Paraskevi Nousi; Nikolaos Stergioulas; Panagiotis Iosif; Alexandra E. Koloniari; et al. (CoRR, 2022)

Rapid Parameter Estimation for Extreme Mass Ratio Inspirals Using Machine Learning. - Bo Liang; Hong Guo; Tianyu Zhao; He Wang 018; Herik Evangelinelis; Yuxiang Xu; Chang Liu; Manjia Liang; Xiaotong Wei; Yong Yuan; Peng Xu; Minghui Du; et al. (CoRR, 2024)

Rapid parameter estimation for merging massive black hole binaries using continuous normalizing flows. - Bo Liang; Minghui Du; He Wang 018; Yuxiang Xu; Chang Liu; Xiaotong Wei; Peng Xu; Li-E. Qiang; Ziren Luo (Mach. Learn. Sci. Technol., 2024)

WaveFormer: transformer-based denoising method for gravitational-wave data. - He Wang 018; Yue Zhou 014; Zhoujian Cao; Zong-Kuan Guo; Zhixiang Ren (Mach. Learn. Sci. Technol., 2024)