34 citations · 134 across the 24 of their papers we have counts for
8 papers · 1 filter
Smart Scheduling based on Deep Reinforcement Learning for Cellular Networks
Jian Wang, Chen Xu, Rong Li +2
To improve the system performance towards the Shannon limit, advanced radio resource management mechanisms play a fundamental role. In particular, scheduling should receive much at…
Attention-Aware Answers of the Crowd
Jingzheng Tu, Guoxian Yu, Jun Wang +2
Crowdsourcing is a relatively economic and efficient solution to collect annotations from the crowd through online platforms. Answers collected from workers with different expertis…
Multi-View Multiple Clusterings using Deep Matrix Factorization
Shaowei Wei, Jun Wang, Guoxian Yu +2
Multi-view clustering aims at integrating complementary information from multiple heterogeneous views to improve clustering results. Existing multi-view clustering solutions can on…
Cross-modal Zero-shot Hashing
Xuanwu Liu, Zhao Li, Jun Wang +3
Hashing has been widely studied for big data retrieval due to its low storage cost and fast query speed. Zero-shot hashing (ZSH) aims to learn a hashing model that is trained using…
Multi-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization
Yuying Xing, Guoxian Yu, Carlotta Domeniconi +3
Multi-view Multi-instance Multi-label Learning(M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple…
Multiple Independent Subspace Clusterings
Xing Wang, Jun Wang, Carlotta Domeniconi +3
Multiple clustering aims at discovering diverse ways of organizing data into clusters. Despite the progress made, it's still a challenge for users to analyze and understand the dis…