most citedScalable Learning Paradigms for Data-Driven Wireless Communication

2 citations · 4 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG2021

Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey

Kai Chen, Qinglei Kong, Yijue Dai +4

Data-driven paradigms are well-known and salient demands of future wireless communication. Empowered by big data and machine learning, next-generation data-driven communication sys…

cs.LG2020

Graph Neural Network for Large-Scale Network Localization

Wenzhong Yan, Di Jin, Zhidi Lin +1

Graph neural networks (GNNs) are popular to use for classifying structured data in the context of machine learning. But surprisingly, they are rarely applied to regression problems…

cs.LG20202 cited

Optimally Combining Classifiers for Semi-Supervised Learning

Zhiguo Wang, Liusha Yang, Feng Yin +3

This paper considers semi-supervised learning for tabular data. It is widely known that Xgboost based on tree model works well on the heterogeneous features while transductive supp…

cs.DC2020

FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing

Feng Yin, Zhidi Lin, Yue Xu +5

In this overview paper, data-driven learning model-based cooperative localization and location data processing are considered, in line with the emerging machine learning and big da…

cs.LG20202 cited

Scalable Learning Paradigms for Data-Driven Wireless Communication

Yue Xu, Feng Yin, Wenjun Xu +3

The marriage of wireless big data and machine learning techniques revolutionizes the wireless system by the data-driven philosophy. However, the ever exploding data volume and mode…