credibility network
Learning to Parse Images
We describe a class of probabilistic models that we call credibility networks. Using parse trees as internal representations of images, credibility networks are able to perform segmentation and recog(cid:173) nition simultaneously, removing the need for ad hoc segmentation heuristics. Promising results in the problem of segmenting hand(cid:173) written digits were obtained.
News Verification by Exploiting Conflicting Social Viewpoints in Microblogs
Jin, Zhiwei (Institute of Computing Technology, Chinese Academy of Sciences) | Cao, Juan (Institute of Computing Technology, Chinese Academy of Sciences) | Zhang, Yongdong (Institute of Computing Technology, Chinese Academy of Sciences) | Luo, Jiebo (University of Rochester)
Fake news spreading in social media severely jeopardizes the veracity of online content. Fortunately, with the interactive and open features of microblogs, skeptical and opposing voices against fake news always arise along with it. The conflicting information, ignored by existing studies, is crucial for news verification. In this paper, we take advantage of this "wisdom of crowds" information to improve news verification by mining conflicting viewpoints in microblogs. First, we discover conflicting viewpoints in news tweets with a topic model method. Based on identified tweets' viewpoints, we then build a credibility propagation network of tweets linked with supporting or opposing relations. Finally, with iterative deduction, the credibility propagation on the network generates the final evaluation result for news. Experiments conducted on a real-world data set show that the news verification performance of our approach significantly outperforms those of the baseline approaches.
Learning to Parse Images
Hinton, Geoffrey E., Ghahramani, Zoubin, Teh, Yee Whye
We describe a class of probabilistic models that we call credibility networks. Using parse trees as internal representations of images, credibility networks are able to perform segmentation and recognition simultaneously, removing the need for ad hoc segmentation heuristics. Promising results in the problem of segmenting handwritten digits were obtained.
Learning to Parse Images
Hinton, Geoffrey E., Ghahramani, Zoubin, Teh, Yee Whye
We describe a class of probabilistic models that we call credibility networks. Using parse trees as internal representations of images, credibility networks are able to perform segmentation and recognition simultaneously,removing the need for ad hoc segmentation heuristics. Promising results in the problem of segmenting handwritten digitswere obtained.