Improving the generalization via coupled tensor norm regularization

发布时间：2023-05-24 作者：浏览次数：

Speaker:	崔春风教授	DateTime:	2023年5月31日（周三）14:00-15:00
Brief Introduction to Speaker: 崔春风，北京航空航天大学，教授		Place:	腾讯会议：612139884
Abstract:In this paper, we propose a coupled tensor norm regularization that could enable the model output feature and the data input to lie in a low-dimensional manifold, which helps us to reduce overfitting. We show this regularization term is convex, differentiable, and gradient Lipschitz continuous for logistic regression, while nonconvex and nonsmooth for deep neural networks. We further analyze the convergence of the first-order method for solving this model. The numerical experiments demonstrate that our method is efficient.