Learning from crowds with variational Gaussian processes
作者：
Highlights：
• Gaussian Processes are used to address the crowdsourcing problem.
• Variational inference is used for the first time to train the model.
• Annotations provided for test instances can be integrated into the prediction.
• We provide an experimental comparison with state-of-the-art crowdsourcing methods.
• The proposed method outperforms all state-of-the-art methods it was compared against.
摘要
•Gaussian Processes are used to address the crowdsourcing problem.•Variational inference is used for the first time to train the model.•Annotations provided for test instances can be integrated into the prediction.•We provide an experimental comparison with state-of-the-art crowdsourcing methods.•The proposed method outperforms all state-of-the-art methods it was compared against.
论文关键词：Crowdsourcing,Classification,Gaussian processes,Bayesian modeling,Variational inference
论文评审过程：Received 26 February 2018, Revised 21 August 2018, Accepted 17 November 2018, Available online 20 November 2018, Version of Record 29 November 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.11.021
