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Papers matching “overparameterized neural networks” 13

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Learning deep linear neural networks: Riemannian gradient flows and convergence to global minimizers
2021 Information and Inference A Journal of the IMA article Computer Science Stochastic Gradient Optimization Techniques Open access

Bubacarr Bah, Holger Rauhut, Ulrich Terstiege, Michael Westdickenberg

29

citations
Convergence of Gradient Descent for Recurrent Neural Networks: A Nonasymptotic Analysis
2025 SIAM Journal on Mathematics of Data Science article Computer Science Neural Networks and Applications Open access

Semih Çaycı, Atilla Eryılmaz

2

citations
Convergence of Gradient Descent for Recurrent Neural Networks: A Nonasymptotic Analysis
2024 arXiv (Cornell University) preprint Computer Science Stochastic Gradient Optimization Techniques Open access

Semih Çaycı, Atilla Eryılmaz

0

citations
A Riemannian Optimization Perspective of the Gauss-Newton Method for Feedforward Neural Networks
2024 arXiv (Cornell University) preprint Computer Science Stochastic Gradient Optimization Techniques Open access

Semih Çaycı

1

citations
Non-Asymptotic Optimization and Generalization Bounds for Stochastic Gauss-Newton in Overparameterized Models
2025 arXiv (Cornell University) preprint Computer Science Stochastic Gradient Optimization Techniques Open access

Semih Çaycı

0

citations
Double descent: When do neural quantum states generalize?
2026 Physical review. E article Physics and Astronomy Quantum many-body systems Open access

M. Schuyler Moss, Alev Orfi, Anirvan M. Sengupta, Antoine Georges, Dries Sels, Anna Dawid, +1 more

1

citations
Does Compression Imply Generalization? A Minimum Description Length Perspective
2026 arXiv (Cornell University) preprint Computer Science Stochastic Gradient Optimization Techniques Open access

Lincen Yang, Jiayang Shi, Nan Pu, Daniël M. Pelt, Matthijs van Leeuwen, Zhong Li, +1 more

0

citations
Teacher Geometry Shapes Learnability in Teacher-Student Networks
2026 arXiv (Cornell University) preprint Computer Science Neural Networks and Applications Open access

Kai Jappe Sandbrink, Flavio Martinelli, Alexander van Meegen, Wulfram Gerstner, Johanni Brea

0

citations
Preference-Based Reward Learning under Partial Observability with Inexact Dynamics
2026 arXiv (Cornell University) preprint Computer Science Reinforcement Learning in Robotics Open access

Reza Zolnouri, Semih Çaycı

0

citations
More is less: inducing sparsity via overparameterization
2023 Information and Inference A Journal of the IMA article Engineering Sparse and Compressive Sensing Techniques

Hung-Hsu Chou, Johannes Maly, Holger Rauhut

9

citations
Double Descent in Quantum Kernel Methods
2025 PRX Quantum article Computer Science Quantum Computing Algorithms and Architecture Open access

Evert van Nieuwenburg

1

citations
More is Less: Inducing Sparsity via Overparameterization
2021 arXiv (Cornell University) preprint Engineering Sparse and Compressive Sensing Techniques Open access

Hung-Hsu Chou, Johannes Maly, Holger Rauhut

1

citations
Overparameterization and generalization error: weighted trigonometric interpolation
2020 arXiv (Cornell University) preprint Computer Science Stochastic Gradient Optimization Techniques Open access

Yuege Xie, Hung-Hsu Chou, Holger Rauhut, Rachel Ward

0

citations

13 results