Cite
Scalable and memory-efficient sparse learning for classification with approximate Bayesian regularization priors.
MLA
Luo, Jiahua, et al. “Scalable and Memory-Efficient Sparse Learning for Classification with Approximate Bayesian Regularization Priors.” Neurocomputing, vol. 457, Oct. 2021, pp. 106–16. EBSCOhost, https://doi.org/10.1016/j.neucom.2021.06.025.
APA
Luo, J., Gan, Y., Vong, C.-M., Wong, C.-M., & Chen, C. (2021). Scalable and memory-efficient sparse learning for classification with approximate Bayesian regularization priors. Neurocomputing, 457, 106–116. https://doi.org/10.1016/j.neucom.2021.06.025
Chicago
Luo, Jiahua, Yanfen Gan, Chi-Man Vong, Chi-Man Wong, and Chuangquan Chen. 2021. “Scalable and Memory-Efficient Sparse Learning for Classification with Approximate Bayesian Regularization Priors.” Neurocomputing 457 (October): 106–16. doi:10.1016/j.neucom.2021.06.025.