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# NAS-Bench-ASR <ref>Mehrotra, A., Ramos, A. G. C., Bhattacharya, S., Dudziak, Ł., Vipperla, R., Chau, T., ... & Lane, N. D. (2020, September). Nas-bench-asr: Reproducible neural architecture search for speech recognition. In International Conference on Learning Representations.</ref>
# NAS-Bench-NLP<ref>Klyuchnikov, N., Trofimov, I., Artemova, E., Salnikov, M., Fedorov, M., & Burnaev, E. (2020). NAS-Bench-NLP: neural architecture search benchmark for natural language processing. arXiv preprint arXiv:2006.07116.</ref>
# TransNAS-Bench-101<ref>Duan, Y., Chen, X., Xu, H., Chen, Z., Liang, X., Zhang, T., & Li, Z. (2021). Transnas-bench-101: Improving transferability and generalizability of cross-task neural architecture search. In ''Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition'' (pp. 5251-5260).</ref>
# LC-Bench <ref>Zimmer, L., Lindauer, M., & Hutter, F. (2021). Auto-Pytorch: multi-fidelity metalearning for efficient and robust autoDL. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 3079-3090.</ref>
# NAS-Bench-x11 <ref>Yan, S., White, C., Savani, Y., & Hutter, F. (2021). NAS-Bench-x11 and the Power of Learning Curves. Advances in Neural Information Processing Systems, 34.</ref>
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