[1] Ben Adlam and Jeffrey Pennington. The neural tangent kernel in high dimensions: Triple descent and a multi-scale theory of generalization. In International Conference on Machine Learning, pages 74-84. PMLR, 2020.
[2] Zeyuan Allen-Zhu, Yuanzhi Li, and Yingyu Liang. Learning and generalization in overparameterized neural networks, going beyond two layers. Advances in neural information processing systems, 32, 2019.
[3] Maksym Andriushchenko, Aditya Vardhan Varre, Loucas Pillaud-Vivien, and Nicolas Flammarion. Sgd with large step sizes learns sparse features. In International Conference on Machine Learning, pages 903–925. PMLR, 2023.
[4] Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, Russ R Salakhutdinov, and Ruosong Wang. On exact computation with an infinitely wide neural net. Advances in Neural Information Processing Systems, 32, 2019.
[5] Alexander Atanasov, Blake Bordelon, and Cengiz Pehlevan. Neural networks as kernel learners: The silent alignment effect. In International Conference on Learning Representations, 2022.
[6] Jimmy Ba, Murat A Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu, and Greg Yang. High-dimensional asymptotics of feature learning: How one gradient step improves the representation. Advances in Neural Information Processing Systems, 35:37932-37946, 2022.
[7] Yu Bai and Jason D. Lee. Beyond linearization: On quadratic and higher-order approximation of wide neural networks. In International Conference on Learning Representations, 2020.
[8] Zhidong Bai and Jack W Silverstein. Spectral analysis of large dimensional random matrices, volume 20. Springer, 2010.
[9] Jinho Baik, Gérard Ben Arous, and Sandrine Péché. Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices. The Annals of Probability, 33(5):1643-1697, 2005.
[10] Aristide Baratin, Thomas George, César Laurent, R Devon Hjelm, Guillaume Lajoie, Pascal Vincent, and Simon Lacoste-Julien. Implicit regularization via neural feature alignment. In International Conference on Artificial Intelligence and Statistics, pages 2269-2277. PMLR, 2021.
[11] Peter L Bartlett, Andrea Montanari, and Alexander Rakhlin. Deep learning: a statistical viewpoint. Acta numerica, 30:87-201, 2021.
[12] Mikhail Belkin, Siyuan Ma, and Soumik Mandal. To understand deep learning we need to understand kernel learning. In International Conference on Machine Learning, pages 541-549. PMLR, 2018.
[13] Lucas Benigni and Sandrine Péché. Eigenvalue distribution of some nonlinear models of random matrices. Electronic Journal of Probability, 26:1-37, 2021.
[14] Lucas Benigni and Sandrine Péché. Largest eigenvalues of the conjugate kernel of single-layered neural networks. arXiv preprint arXiv:2201.04753, 2022.
[15] Gaspard Beugnot, Julien Mairal, and Alessandro Rudi. On the benefits of large learning rates for kernel methods. In Conference on Learning Theory, pages 254–282. PMLR, 2022.
[16] Alberto Bietti and Julien Mairal. On the inductive bias of neural tangent kernels. In Advances in Neural Information Processing Systems, pages 12873-12884, 2019.
[17] David Bosch, Ashkan Panahi, and Babak Hassibi. Precise asymptotic analysis of deep random feature models. arXiv preprint arXiv:2302.06210, 2023.
[18] Sourav Chatterjee. Convergence of gradient descent for deep neural networks. arXiv preprint arXiv:2203.16462, 2022.