Generative Models for Learning from Crowds

Chi Hong

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arXiv · 1706.03930

Generative Models for Learning from Crowds

Chi Hong

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Abstract

In this paper, we propose generative probabilistic models for label aggregation. We use Gibbs sampling and a novel variational inference algorithm to perform the posterior inference. Empirical results show that our methods consistently outperform state-of-the-art methods.

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Chi Hong. 2017-06-13. Generative Models for Learning from Crowds. https://arxiv.org/abs/1706.03930

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