2 citations · 2 across the 3 of their papers we have counts for
7 papers · 1 filter
Output-Dependent Gaussian Process State-Space Model
Zhidi Lin, Lei Cheng, Feng Yin +2
Gaussian process state-space model (GPSSM) is a fully probabilistic state-space model that has attracted much attention over the past decade. However, the outputs of the transition…
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…
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…
Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification
Yue Xu, Feng Yin, Wenjun Xu +2
The cloud radio access network (C-RAN) is a promising paradigm to meet the stringent requirements of the fifth generation (5G) wireless systems. Meanwhile, wireless traffic predict…
Multitask Gaussian Process with Hierarchical Latent Interactions
Kai Chen, Twan van Laarhoven, Elena Marchiori +2
Multitask Gaussian process (MTGP) is powerful for joint learning of multiple tasks with complicated correlation patterns. However, due to the assembling of additive independent lat…
Compressible Spectral Mixture Kernels with Sparse Dependency Structures for Gaussian Processes
Kai Chen, Yijue Dai, Feng Yin +2
Spectral mixture (SM) kernels comprise a powerful class of generalized kernels for Gaussian processes (GPs) to describe complex patterns. This paper introduces model compression an…