Improved deep learning techniques in gravitational-wave data analysis. - Heming Xia; Lijing Shao; Junjie Zhao; Zhoujian Cao (CoRR, 2020)

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)

DECODE: DilatEd COnvolutional neural network for Detecting Extreme-mass-ratio inspirals. - Tianyu Zhao; Yue Zhou 014; Ruijun Shi; Zhoujian Cao; Zhixiang Ren (CoRR, 2023)

Compact Binary Systems Waveform Generation with Generative Pre-trained Transformer. - Ruijun Shi; Yue Zhou 014; Tianyu Zhao; Zhoujian Cao; Zhixiang Ren (CoRR, 2023)

GWAI: Artificial intelligence platform for enhanced gravitational wave data analysis. - Tianyu Zhao; Yue Zhou 014; Ruijun Shi; Zhoujian Cao; Zhixiang Ren (SoftwareX, 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)