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References (19)

[1]
Generalization or Hallucination? Understanding Out-of-Context Reasoning in Transformers
2025Yixiao Huang, Hanlin Zhu et al.
[2]
Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions
2022Arthur Jacot
[3]
SGD and Weight Decay Secretly Minimize the Rank of Your Neural Network
2022Tomer Galanti, Zachary S. Siegel et al.
[4]
The Implicit Bias of Gradient Descent on Generalized Gated Linear Networks
2022Samuel Lippl, L. Abbott et al.
[5]
Implicit Regularization Towards Rank Minimization in ReLU Networks
2022Nadav Timor, Gal Vardi et al.
[6]
Directional convergence and alignment in deep learning
2020Ziwei Ji, Matus Telgarsky
[7]
Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian
2019Samet Oymak, Zalan Fabian et al.
[8]
Implicit Regularization in Deep Matrix Factorization
2019Sanjeev Arora, Nadav Cohen et al.
[9]
Gradient descent aligns the layers of deep linear networks
2018Ziwei Ji, Matus Telgarsky
[10]
The Emergence of Spectral Universality in Deep Networks
2018Jeffrey Pennington, S. Schoenholz et al.
[11]
Sensitivity and Generalization in Neural Networks: an Empirical Study
2018Roman Novak, Yasaman Bahri et al.
[12]
Implicit Regularization in Matrix Factorization
2017Suriya Gunasekar, Blake E. Woodworth et al.
[13]
Deep Information Propagation
2016S. Schoenholz, J. Gilmer et al.
[14]
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
2013Andrew M. Saxe, James L. McClelland et al.
[15]
Products of random matrices.
2002A. D. Jackson, B. Lautrup et al.
[16]
Matrix Perturbation Theory
1991V. N. Bogaevski, A. Povzner
[17]
The distribution of Lyapunov exponents: Exact results for random matrices
1986C. Newman
[18]
The Rotation of Eigenvectors by a Perturbation. III
1970Chandler Davis, W. Kahan
[19]
A multiplicative ergodic theorem: Lyapunov characteristic num-bers for dynamical systems
1968V. I. Oseledec

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