1. SENSOY, Murat; KAPLAN, Lance; KANDEMIR, Melih. Evidential deep learning to quantify classification uncertainty. Advances in neural information processing systems, 2018, 31.(먼저 볼 것!) → 디리클레 분포로 넘어가서 불확실성을 정량화 하기 위해서 증거 이론을 기반으로 점 추정에서 변경하도록 함 (OOD)에도 강건하게 모른다라고 말할 수 있도록 하기 위함.
2. AMINI, Alexander, et al. Deep evidential regression. Advances in neural information processing systems, 2020, 33: 14927-14937.(제일 나중에 볼 것)

https://www.youtube.com/watch?si=nLNQD_uDfEyGUKzk&v=toTcf7tZK8c&feature=youtu.be

1. HAN, Zongbo, et al. Trusted multi-view classification with dynamic evidential fusion. IEEE transactions on pattern analysis and machine intelligence, 2022, 45.2: 2551-2566. (파급력 강함)
2. LIU, Wei, et al. Trusted multi-view deep learning with opinion aggregation. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2022. p. 7585-7593.
3. YUE, Xiaodong, et al. Evidential dissonance measure in robust multi-view classification to resist adversarial attack. Information Fusion, 2025, 113: 102605.(adversarial)