[17] Hendrik Alexander Mehrtens, Camila González, and Anirban Mukhopadhyay. Improving robustness and calibration in ensembles with diversity regularization. arXiv preprint arXiv:2201.10908, 2022.
[18] Xiaohui Zhang, Daniel Povey, and Sanjeev Khudanpur. A diversity-penalizing ensemble training method for deep learning. In Sixteenth Annual Conference of the International Speech Communication Association, 2015.
[19] Hong-Jie Xing and Xi-Zhao Wang. Selective ensemble of svdds with renyi entropy based diversity measure. Pattern Recognition, 61:185-196, 2017.
[20] Bruce E Rosen. Ensemble learning using decorrelated neural networks. Connection science, 8 (3-4):373-384, 1996.
[21] Yong Liu and Xin Yao. Ensemble learning via negative correlation. *Neural networks*, 12(10): 1399–1404, 1999.
[22] Gavin Brown, Jeremy L Wyatt, Peter Tino, and Yoshua Bengio. Managing diversity in regression ensembles. Journal of machine learning research, 6(9), 2005.
[23] Zenglin Shi, Le Zhang, Yun Liu, Xiaofeng Cao, Yangdong Ye, Ming-Ming Cheng, and Guoyan Zheng. Crowd counting with deep negative correlation learning. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5382-5390, 2018.
[24] Sebastian Buschjäger, Lukas Pfahler, and Katharina Morik. Generalized negative correlation learning for deep ensembling. arXiv preprint arXiv:2011.02952, 2020.
[25] Francesco D'Angelo and Vincent Fortuin. Repulsive deep ensembles are bayesian. Advances in Neural Information Processing Systems, 34:3451-3465, 2021.
[26] Pierre Baldi and Peter J Sadowski. Understanding dropout. Advances in neural information processing systems, 26, 2013.
[27] Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, and Mihaela van der Schaar. Tangos: Regularizing tabular neural networks through gradient orthogonalization and specialization. arXiv preprint arXiv:2303.05506, 2023.
[28] Yeming Wen, Dustin Tran, and Jimmy Ba. Batchensemble: an alternative approach to efficient ensemble and lifelong learning. arXiv preprint arXiv:2002.06715, 2020.
[29] Anuvabh Dutt, Denis Pellerin, and Georges Quenot. Coupled ensembles of neural networks. Neurocomputing, 396:346-357, 2020.
[30] Andrew Webb, Charles Reynolds, Wenlin Chen, Henry Reeve, Dan Iliescu, Mikel Lujan, and Gavin Brown. To ensemble or not ensemble: When does end-to-end training fail? In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 109-123. Springer, 2020.
[31] 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.
[32] Taiga Abe, E Kelly Buchanan, Geoff Pleiss, and John P Cunningham. Pathologies of predictive diversity in deep ensembles. arXiv preprint arXiv:2302.00704, 2023.
[33] Taiga Abe, E Kelly Buchanan, Geoff Pleiss, and John Patrick Cunningham. The best deep ensembles sacrifice predictive diversity. In I Can't Believe It's Not Better Workshop: Understanding Deep Learning Through Empirical Falsification, 2022.
[34] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115:211-252, 2015.