[35] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
[36] Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700-4708, 2017.
[37] Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun. Shufflenet v2: Practical guidelines for efficient cnn architecture design. In Proceedings of the European conference on computer vision (ECCV), pages 116-131, 2018.
[38] Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer. SqueezeNet: Alexnet-level accuracy with 50x fewer parameters and  $0.5\mathrm{mb}$  model size. arXiv preprint arXiv:1602.07360, 2016.
[39] Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, et al. Searching for mobilenetv3. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1314-1324, 2019.
[40] Sachin Mehta and Mohammad Rastegari. Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer. arXiv preprint arXiv:2110.02178, 2021.
[41] Tom Heskes. Selecting weighting factors in logarithmic opinion pools. Advances in neural information processing systems, 10, 1997.
[42] Georgiana Neculae, Oliver Rhodes, and Gavin Brown. Ensembles of spiking neural networks. arXiv preprint arXiv:2010.14619, 2020.
[43] Gavin Brown and Ludmila I Kuncheva. "good" and "bad" diversity in majority vote ensembles. In International workshop on multiple classifier systems, pages 124-133. Springer, 2010.
[44] Zhengshen Jiang, Hongzhi Liu, Bin Fu, and Zhonghai Wu. Generalized ambiguity decompositions for classification with applications in active learning and unsupervised ensemble pruning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 31, 2017.
[45] Gavin Brown, Jeremy Wyatt, Rachel Harris, and Xin Yao. Diversity creation methods: a survey and categorisation. Information fusion, 6(1):5-20, 2005.
[46] Léo Grinsztajn, Edouard Oyallon, and Gael Varoquaux. Why do tree-based models still outperform deep learning on tabular data? arXiv preprint arXiv:2207.08815, 2022.
[47] Andrés Masegosa, Stephan Lorenzen, Christian Igel, and Yevgeny Seldin. Second order pac-bayesian bounds for the weighted majority vote. Advances in Neural Information Processing Systems, 33:5263-5273, 2020.
[48] Luis A Ortega, Rafael Cabanas, and Andres Masegosa. Diversity and generalization in neural network ensembles. In International Conference on Artificial Intelligence and Statistics, pages 11720-11743. PMLR, 2022.
[49] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32, 2019.
[50] Arthur Asuncion and David Newman. Uci machine learning repository, 2007.
[51] Francesca Grisoni, Viviana Consonni, Sara Villa, Marco Vighi, and Roberto Todeschini. Qsar models for bioconcentration: is the increase in the complexity justified by more accurate predictions? Chemosphere, 127:171-179, 2015.