[26] Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems, 30, 2017. 2
[27] Kimin Lee, Changho Hwang, KyoungSoo Park, and Jinwoo Shin. Confident multiple choice learning. In International Conference on Machine Learning, pages 2014–2023. PMLR, 2017. 2
[28] Stefan Lee, Senthil Purushwalkam, Michael Cogswell, David Crandall, and Dhruv Batra. Why m heads are better than one: Training a diverse ensemble of deep networks. arXiv preprint arXiv:1511.06314, 2015. 2
[29] Stefan Lee, Senthil Purushwalkam Shiva Prakash, Michael Cogswell, Viresh Ranjan, David Crandall, and Dhruv Batra. Stochastic multiple choice learning for training diverse deep ensembles. Advances in Neural Information Processing Systems, 29, 2016. 1, 2, 3
[30] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2018. 15
[31] David JC MacKay. A practical bayesian framework for backpropagation networks. Neural computation, 4(3):448-472, 1992. 2
[32] Osama Makansi, Eddy Ilg, Ozgun Cicek, and Thomas Brox. Overcoming limitations of mixture density networks: A sampling and fitting framework for multimodal future prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7144-7153, 2019. 1, 2, 16
[33] Saeed Masoudnia and Reza Ebrahimpour. Mixture of experts: a literature survey. The Artificial Intelligence Review, 42(2):275, 2014. 3
[34] Christian Rupprecht, Iro Laina, Robert DiPietro, Maximilian Baust, Federico Tombari, Nassir Navab, and Gregory D Hager. Learning in an uncertain world: Representing ambiguity through multiple hypotheses. In Proceedings of the IEEE international conference on computer vision, pages 3591-3600, 2017. 1, 2, 3, 5, 14
[35] Justin Salamon, Christopher Jacoby, and Juan Pablo Bello. A dataset and taxonomy for urban sound research. In Proceedings of the 22nd ACM international conference on Multimedia, pages 1041-1044, 2014. 17
[36] Christopher Schymura, Benedikt Bonninghoff, Tsubasa Ochiai, Marc Delcroix, Keisuke Kinoshita, Tomohiro Nakatani, Shoko Araki, and Dorothea Kolossa. PILOT: Introducing transformers for probabilistic sound event localization. In *Interspeech 2021*, pages 2117-2121. ISCA, 2021. 14, 15, 16
[37] Christopher Schymura, Tsubasa Ochiai, Marc Delcroix, Keisuke Kinoshita, Tomohiro Nakatani, Shoko Araki, and Dorothea Kolossa. Exploiting attention-based sequence-to-sequence architectures for sound event localization. In 2020 28th European Signal Processing Conference (EUSIPCO), pages 231-235. IEEE, 2021. 7, 8, 14, 15, 16
[38] John Shore and Rodney Johnson. Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. IEEE Transactions on information theory, 26(1):26-37, 1980. 5
[39] Hao Song, Tom Diethe, Meelis Kull, and Peter Flach. Distribution calibration for regression. In International Conference on Machine Learning, pages 5897-5906. PMLR, 2019. 10
[40] Aswin Shanmugam Subramanian, Chao Weng, Shinji Watanabe, Meng Yu, and Dong Yu. Deep learning based multi-source localization with source splitting and its effectiveness in multi-talker speech recognition. Computer Speech & Language, 75:101360, 2022. 7
[41] Harshavardhan Sundar, Weiran Wang, Ming Sun, and Chao Wang. Raw waveform based end-to-end deep convolutional network for spatial localization of multiple acoustic sources. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4642-4646. IEEE, 2020. 7