Paper links active learning with lottery ticket sparsity for efficient network training

A newly posted arXiv paper explores combining active learning with the lottery ticket hypothesis, asking whether sparse subnetworks that match dense-model accuracy can be found while also reducing labeled data needs. The authors frame this as achieving both sparsity and sample efficiency within a single training process rather than running separate search stages. The work appears in both the machine learning and AI categories of arXiv.