57 citations · 147 across the 5 of their papers we have counts for
5 papers
Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations
Jie Chen, Nannan Cao, Kian Hsiang Low +3
Gaussian processes (GP) are Bayesian non-parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-t…
Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena
Jie Chen, Kian Hsiang Low, Colin Keng-Yan Tan +4
The problem of modeling and predicting spatiotemporal traffic phenomena over an urban road network is important to many traffic applications such as detecting and forecasting conge…
Gaussian Process-Based Decentralized Data Fusion and Active Sensing for Mobility-on-Demand System
Jie Chen, Kian Hsiang Low, Colin Keng-Yan Tan
Mobility-on-demand (MoD) systems have recently emerged as a promising paradigm of one-way vehicle sharing for sustainable personal urban mobility in densely populated cities. In th…
Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations
Jie Chen, Nannan Cao, Kian Hsiang Low +3
Gaussian processes (GP) are Bayesian non-parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-t…
Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena
Jie Chen, Kian Hsiang Low, Colin Keng-Yan Tan +4
The problem of modeling and predicting spatiotemporal traffic phenomena over an urban road network is important to many traffic applications such as detecting and forecasting conge…