1. Baba, Y., Li, J., Kashima, H.: Crowdea: multi-view idea prioritization with crowds. Proc. AAAI Conf. Human Comput. Crowdsourcing (HCOMP) 8(1), 23–32 (2020). https://doi.org/10.1609/hcomp.v8i1.7460
2. Bachrach, Y., Minka, T., Guiver, J., Graepel, T.: How to grade a test without knowing the answers: a bayesian graphical model for adaptive crowdsourcing and aptitude testing. In: Proceedings of the 29th International Coference on International Conference on Machine Learning (ICML), pp. 819–826 (2012), https://dl.acm.org/doi/abs/10.5555/3042573.3042680
3. Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: Proceedings of the 37th International Conference on Machine Learning (ICML) (2020). https://dl.acm.org/doi/abs/10.5555/3524938.3525087
4. Dawid, A.P., Skene, A.M.: Maximum likelihood estimation of observer error-rates using the EM algorithm. J. Royal Stat. Society. Series C (Applied Statistics) 28(1), 20–28 (1979). https://doi.org/10.2307/2346806
5. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4171–4186 (2019). https://doi.org/10.18653/v1/N19-1423