[71] Samet Oymak and Mahdi Soltanolkotabi. Toward moderate overparameterization: Global convergence guarantees for training shallow neural networks. IEEE Journal on Selected Areas in Information Theory, 1(1):84-105, 2020.
[72] Jeffrey Pennington and Yasaman Bahri. Geometry of neural network loss surfaces via random matrix theory. In International Conference on Machine Learning, pages 2798-2806. PMLR, 2017.
[73] Jeffrey Pennington and Pratik Worah. Nonlinear random matrix theory for deep learning. Advances in neural information processing systems, 30, 2017.
[74] Jeffrey Pennington and Pratik Worah. The spectrum of the fisher information matrix of a single-hidden-layer neural network. Advances in neural information processing systems, 31, 2018.
[75] Bartlomiej Polaczyk and Jacek Cyranka. Improved overparametrization bounds for global convergence of sgd for shallow neural networks. Transactions on Machine Learning Research, 2022.
[76] Maria Refinetti, Sebastian Goldt, Florent Krzakala, and Lenka Zdeborova. Classifying high-dimensional gaussian mixtures: Where kernel methods fail and neural networks succeed. In International Conference on Machine Learning, pages 8936-8947. PMLR, 2021.
[77] Dominik Schröder, Hugo Cui, Daniil Dmitriev, and Bruno Loureiro. Deterministic equivalent and error universality of deep random features learning. In Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pages 30285-30320. PMLR, 23-29 Jul 2023.
[78] Haozhe Shan and Blake Bordelon. A theory of neural tangent kernel alignment and its influence on training. arXiv preprint arXiv:2105.14301, 2021.
[79] Matthias Thamm, Max Staats, and Bernd Rosenow. Random matrix analysis of deep neural network weight matrices. Physical Review E, 106(5):054124, 2022.
[80] Roman Vershynin. High-dimensional probability: An introduction with applications in data science, volume 47. Cambridge university press, 2018.
[81] Zhichao Wang and Yizhe Zhu. Deformed semicircle law and concentration of nonlinear random matrices for ultra-wide neural networks. arXiv preprint arXiv:2109.09304, 2021.
[82] Alexander Wei, Wei Hu, and Jacob Steinhardt. More than a toy: Random matrix models predict how real-world neural representations generalize. In Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 23549-23588. PMLR, 17-23 Jul 2022.
[83] Christopher Williams. Computing with infinite networks. Advances in neural information processing systems, 9, 1996.
[84] Greg Yang. Wide feedforward or recurrent neural networks of any architecture are gaussian processes. Advances in Neural Information Processing Systems, 32, 2019.
[85] Greg Yang and Edward J Hu. Tensor programs iv: Feature learning in infinite-width neural networks. In International Conference on Machine Learning, pages 11727-11737. PMLR, 2021.
[86] Yaoqing Yang, Ryan Theisen, Liam Hodgkinson, Joseph E Gonzalez, Kannan Ramchandran, Charles H Martin, and Michael W Mahoney. Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data. arXiv preprint arXiv:2202.02842, 2022.
[87] Jacob A Zavatone-Veth, William L Tong, and Cengiz Pehlevan. Contrasting random and learned features in deep bayesian linear regression. Physical Review E, 105(6):064118, 2022.
[88] Difan Zou, Yuan Cao, Dongruo Zhou, and Quanquan Gu. Gradient descent optimizes overparameterized deep relu networks. Machine learning, 109:467-492, 2020.