American Psychological Association 6th edition

Qin, C., Zhang, Y., & Cao, Y. (2025). Large-scale machine learning with synchronous parallel adaptive stochastic variance reduction gradient descent for high-dimensional blindness detection on spark. The Journal of Supercomputing: An International Journal of High-Performance Computer Design, Analysis, and Use, 81(4). https://doi.org/10.1007/s11227-025-07046-8

ISO-690 (author-date, English)

QIN, Chuandong, ZHANG, Yiqing and CAO, Yu, 2025. Large-scale machine learning with synchronous parallel adaptive stochastic variance reduction gradient descent for high-dimensional blindness detection on spark. The Journal of Supercomputing: An International Journal of High-Performance Computer Design, Analysis, and Use. 1 March 2025. Vol. 81, no. 4, . DOI 10.1007/s11227-025-07046-8.

Modern Language Association 9th edition

Qin, C., Y. Zhang, and Y. Cao. “Large-Scale Machine Learning With Synchronous Parallel Adaptive Stochastic Variance Reduction Gradient Descent for High-Dimensional Blindness Detection on Spark”. The Journal of Supercomputing: An International Journal of High-Performance Computer Design, Analysis, and Use, vol. 81, no. 4, Mar. 2025, https://doi.org/10.1007/s11227-025-07046-8.

Mohr Siebeck - Recht (Deutsch - Österreich)

Qin, Chuandong/Zhang, Yiqing/Cao, Yu: Large-scale machine learning with synchronous parallel adaptive stochastic variance reduction gradient descent for high-dimensional blindness detection on spark, The Journal of Supercomputing: An International Journal of High-Performance Computer Design, Analysis, and Use 2025,

Emerald - Harvard

Qin, C., Zhang, Y. and Cao, Y. (2025), “Large-scale machine learning with synchronous parallel adaptive stochastic variance reduction gradient descent for high-dimensional blindness detection on spark”, The Journal of Supercomputing: An International Journal of High-Performance Computer Design, Analysis, and Use, Vol. 81 No. 4, available at:https://doi.org/10.1007/s11227-025-07046-8.

Warning: These citations may not always be 100% accurate.