no code implementations • 5 Mar 2022 • Qifan Wang, Yi Fang, Anirudh Ravula, Ruining He, Bin Shen, Jingang Wang, Xiaojun Quan, Dongfang Liu
Network embedding is an effective technique to learn the low-dimensional representations of nodes in networks.
no code implementations • 30 Dec 2018 • Xianghong Fang, Haoli Bai, Ziyi Guo, Bin Shen, Steven Hoi, Zenglin Xu
In this paper, we propose a new unsupervised domain adaptation method named Domain-Adversarial Residual-Transfer (DART) learning of Deep Neural Networks to tackle cross-domain image classification tasks.
5 code implementations • 29 Apr 2018 • Travis Ebesu, Bin Shen, Yi Fang
We propose Collaborative Memory Networks (CMN), a deep architecture to unify the two classes of CF models capitalizing on the strengths of the global structure of latent factor model and local neighborhood-based structure in a nonlinear fashion.
no code implementations • 9 Jun 2014 • Xue Li, Yu-Jin Zhang, Bin Shen, Bao-Di Liu
A novel tag completion algorithm is proposed in this paper, which is designed with the following features: 1) Low-rank and error s-parsity: the incomplete initial tagging matrix D is decomposed into the complete tagging matrix A and a sparse error matrix E. However, instead of minimizing its nuclear norm, A is further factor-ized into a basis matrix U and a sparse coefficient matrix V, i. e. D=UV+E.