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Found 4 papers, 1 papers with code
Deep Partial Multiplex Network Embedding
Network embedding is an effective technique to learn the low-dimensional representations of nodes in networks.
DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification
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.
Collaborative Memory Network for Recommendation Systems
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.
Image Tag Completion by Low-rank Factorization with Dual Reconstruction Structure Preserved
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.
