[48] A. Falkner, G. Friedrich, K. Schekotihin, R. Taupe, and E. Teppan. Industrial applications of answer set programming. *KI-Künstliche Intelligenz*, pages 1–12, 2018.
[49] X. Dong, M. Tan, A. Yu, D. Peng, B. Gabrys, and Q. Le. Autohas: Differentiable hyperparameter and architecture search. arXiv:2006.03656 [cs.CV], 2020.
[50] Justin M. Gilmer, George E. Dahl, and Zachary Nado. init2winit: a javax codebase for initialization, optimization, and tuning research, 2021. URL http://github.com/google/init2winit.
[51] Y. Nesterov. A method of solving a convex programming problem with convergence rate  $\mathrm{O}(1 / \mathrm{sqr}(\mathbf{k}))$ . Soviet Mathematics Doklady, 27:372-376, 1983.
[52] Aurko Roy, Mohammad Saffar, Ashish Vaswani, and David Grangier. Efficient content-based sparse attention with routing transformers. Transactions of the Association for Computational Linguistics, 9:53-68, 2021.
[53] Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, and Phillip Koehn. One billion word benchmark for measuring progress in statistical language modeling. CoRR, abs/1312.3005, 2013. URL http://arxiv.org/abs/1312.3005.
[54] Benjamin Lefaudeau, Francisco Massa, Diana Liskovich, Wenhan Xiong, Vittorio Caggiano, Sean Naren, Min Xu, Jieru Hu, Marta Tintore, Susan Zhang, Patrick Labatut, and Daniel Haziza. xformers: A modular and hackable transformer modelling library. https://github.com/facebookresearch/xformers, 2022.
[55] Ondrej Bojar, Rajen Chatterjee, Christian Federmann, Barry Haddow, Matthias Huck, Chris Hokamp, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Matt Post, Carolina Scarton, Lucia Specia, and Marco Turchi. Findings of the 2015 workshop on statistical machine translation. In Proceedings of the Tenth Workshop on Statistical Machine Translation, pages 1-46, Lisbon, Portugal, September 2015. Association for Computational Linguistics. URL http://aclweb.org/anthology/W15-3001.
[56] Sergey Zagoruyko and Nikos Komodakis. Wide residual networks. arXiv preprint arXiv:1605.07146, 2016.
[57] A. Krizhevsky. Learning multiple layers of features from tiny images. Technical report, University of Toronto, 2009.
[58] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR'16), pages 770-778. CVF and IEEE CS, IEEE, 2016.
[59] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. Berg, and L. Fei-Fei. Imagenet large scale visual recognition challenge. IJCV, 115(3):211–252, 2015.
[60] T. Chen and C. Guestrin. Xgboost: A scalable tree boosting system. In B. Krishnapuram, M. Shah, A. Smola, C. Aggarwal, D. Shen, and R. Rastogi, editors, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'16), pages 785-794. ACM Press, 2016.
[61] N. Awad, N. Mallik, and F. Hutter. DEHB: Evolutionary hyperband for scalable, robust and efficient Hyperparameter Optimization. In Z. Zhou, editor, Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'21), pages 2147-2153, 2021.
[62] J. Siems, L. Zimmer, A. Zela, J. Lukasik, M. Keuper, and F. Hutter. NAS-bench-301 and the case for surrogate benchmarks for Neural Architecture Search. arXiv:2008.09777v4 [cs.LG], 2020.
[63] B. Bischl, G. Casalicchio, M. Feurer, F. Hutter, M. Lang, R. Mantovani, J. N. van Rijn, and J. Vanschoren. Openml benchmarking suites and the openml100. arXiv:1708.03731v1 [stat.ML], 2019.