PI and EI in Bayesian optimisation under gaussian noise assumption
Bayesian optimisation is a hyper-parameter tunning approach which usually adopts the gaussian process as a surrogate model. Its acquisition functions Probabilit...
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Hello! I am a final year Informatics student at University of Edinburgh under the supervision of Dr Arno Onken. My current research focuses on optimizing the pipeline of deep unsupervised generative models of time series neural activities. Aiming to improve its quality for data augmentation. Besides of Deep Learning, I am also interested in Gaussian processes, such as Bayesian Optimization for hyperparameter-tunning and a widely adopted dimension reduction technique Gaussian Process Factor Analysis (GPFA) in neuroscience. I was a Research Data scientist Intern at Deloitte in Beijing where I took part in the Deloitte INsight project, which aims to track the prosperity index of all thousands of industries in China.
Developing kernels in GPFA
Gaussian process makes it possible to construct probabilistic models over unknown underlying functions. This is useful for modelling temporal continuous traject...
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Human Activity Recognition
An Android App named PDIoT which implements real time human activity recognition, which can classify up to 18 activities and can achieve 95% accuracy on 5 activ...
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