We study a new technique for understanding convergence of learning agents under small modifications of data. We show that such convergence can be understood via an analogue of Fatou's lemma which yields gamma-convergence. We show it's relevance and applications in general machine learning tasks and domain adaptation transfer learning.

翻译：我们研究了一种理解学习主体在数据微小修改下收敛性的新技术。我们证明，这种收敛性可通过法图引理的类比得到理解，从而产生γ-收敛。我们展示了其在通用机器学习任务及域适应迁移学习中的相关性与应用。