Label Selection Approach to Learning from Crowds
Author:
Affiliation:
1. Kyoto University
Publisher
Japanese Society for Artificial Intelligence
Link
https://www.jstage.jst.go.jp/article/tjsai/39/5/39_39-5_F-O23/_pdf
Reference32 articles.
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3. [Braylan 20] Braylan, A. and Lease, M.: Modeling and Aggregation of Complex Annotations via Annotation Distances, in Proceedings of The Web Conference 2020 (WebConf), pp. 1807–1818, Association for Computing Machinery (2020)
4. [Braylan 21] Braylan, A. and Lease, M.: Aggregating Complex Annotations via Merging and Matching, in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 86–94, Association for Computing Machinery (2021)
5. [Chen 20] Chen, Z., Wang, H., Sun, H., Chen, P., Han, T., Liu, X., and Yang, J.: Structured Probabilistic End-to-end Learning from Crowds, in Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI), pp. 1512–1518, International Joint Conferences on Artificial Intelligence Organization (2020)
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