[16] T. Elsken, J. H. Metzen, and F. Hutter, "Efficient multi-objective neural architecture search via lamarckian evolution," in International Conference on Learning Representations, 2019.
[17] M. Javaheripi, G. de Rosa, S. Mukherjee, S. Shah, T. Religa, C. C. Teodoro Mendes, S. Bubeck, F. Koushanfar, and D. Dey, "Litetransformersearch: Training-free neural architecture search for efficient language models," Advances in Neural Information Processing Systems, vol. 35, pp. 24 254-24 267, 2022.
[18] I. Fedorov, R. Matas, H. Tann, C. Zhou, M. Mattina, and P. Whatmough, “Udc: Unified dnas for compressible tinyml models for neural processing units,” Advances in Neural Information Processing Systems, vol. 35, pp. 18456-18471, 2022.
[19] X. Wang, W. Guo, J. Su, X. Yang, and J. Yan, “Zarts: On zero-order optimization for neural architecture search,” in Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds., vol. 35. Curran Associates, Inc., 2022, pp. 12868–12880.
[20] N. Lee, T. Ajanthan, and P. Torr, "Snip: Single-shot network pruning based on connection sensitivity," in International Conference on Learning Representations, 2019.
[21] C. Wang, G. Zhang, and R. Grosse, “Picking winning tickets before training by preserving gradient flow,” in International Conference on Learning Representations, 2020.
[22] H. Tanaka, D. Kunin, D. L. Yamins, and S. Ganguli, "Pruning neural networks without any data by iteratively conserving synaptic flow," Advances in Neural Information Processing Systems, vol. 33, pp. 6377-6389, 2020.
[23] J. Turner, E. J. Crowley, M. O'Boyle, A. Storkey, and G. Gray, "Blockswap: Fisher-guided block substitution for network compression on a budget," in International Conference on Learning Representations, 2020.
[24] W. Chen, X. Gong, and Z. Wang, “Neural architecture search on imagenet in fourgpu hours: A theoretically inspired perspective,” in International Conference on Learning Representations (ICLR), 2021.
[25] Y. Shu, Z. Dai, Z. Wu, and B. K. H. Low, "Unifying and boosting gradient-based training-free neural architecture search," in Advances in Neural Information Processing Systems, A. H. Oh, A. Agarwal, D. Belgrave, and K. Cho, Eds., 2022.
[26] Z. Zhu, F. Liu, G. Chrysos, and V. Cevher, "Generalization properties of nas under activation and skip connection search," Advances in Neural Information Processing Systems, vol. 35, pp. 23551-23565, 2022.
[27] M. Lin, P. Wang, Z. Sun, H. Chen, X. Sun, Q. Qian, H. Li, and R. Jin, “Zen-nas: A zero-shot nas for high-performance image recognition,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 347–356.
[28] A. Jacot, F. Gabriel, and C. Hongler, "Neural tangent kernel: Convergence and generalization in neural networks," Advances in neural information processing systems, vol. 31, 2018.
[29] S. Arora, S. S. Du, W. Hu, Z. Li, R. R. Salakhutdinov, and R. Wang, "On exact computation with an infinitely wide neural net," Advances in neural information processing systems, vol. 32, 2019.
[30] J. Lee, L. Xiao, S. Schoenholz, Y. Bahri, R. Novak, J. Sohl-Dickstein, and J. Pennington, "Wide neural networks of any depth evolve as linear models under gradient descent," Advances in neural information processing systems, vol. 32, 2019.
[31] S. S. Du, X. Zhai, B. Poczos, and A. Singh, "Gradient descent provably optimizes overparameterized neural networks," in International Conference on Learning Representations, 2019.
[32] B. Hanin and M. Nica, “Finite depth and width corrections to the neural tangent kernel,” in International Conference on Learning Representations, 2020.