Data Association with Gaussian Processes
european conference on principles of data mining and knowledge discovery(2020)
摘要
The data association problem is concerned with separating data coming from different generating processes, for example when data comes from different data sources, contain significant noise, or exhibit multimodality. We present a fully Bayesian approach to this problem. Our model is capable of simultaneously solving the data association problem and the induced supervised learning problem. Underpinning our approach is the use of Gaussian process priors to encode the structure of both the data and the data associations. We present an efficient learning scheme based on doubly stochastic variational inference and discuss how it can be applied to deep Gaussian process priors.
更多查看译文
关键词
data association,processes
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络