Publisher
Springer Nature Switzerland
Reference21 articles.
1. Kainen, P.C., Kůrková, V., Sanguineti, M.: Dependence of computational models on input dimension: tractability of approximation and optimization tasks. IEEE Trans. Inf. Theor. 58, 1203–1214 (2012)
2. Telgarsky, M.: Benefits of depth in neural networks. Proc. Mach. Learn. Res. 49, 1517–1539 (2016)
3. Yarotsky, D.: Error bounds for approximations with deep ReLU networks. Neural Netw. 94, 103–114 (2017)
4. Gorban, A., Tyukin, I., Prokhorov, D., Sofeikov, K.: Approximation with random bases: pro et contra. Inf. Sci. 364–365, 129–145 (2016)
5. Gorban, A., Tyukin, I.: Blessing of dimensionality: mathematical foundations of the statistical physics of data. Philos. Trans. Royal Soc. A 376, 2017–2037 (2018)