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Papers matching “overparameterization” 17

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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
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
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, Antoine Georges, Dries Sels, Anna Dawid, Agnes Valenti

1

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

1

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

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

0

citations
Gradient descent for deep matrix factorization: Dynamics and implicit bias towards low rank
2023 Applied and Computational Harmonic Analysis article Computer Science Stochastic Gradient Optimization Techniques

Hung-Hsu Chou, Carsten Gieshoff, Johannes Maly, Holger Rauhut

14

citations
Gradient Descent for Deep Matrix Factorization: Dynamics and Implicit Bias Towards Low Rank
2022 SSRN Electronic Journal preprint Computer Science Stochastic Gradient Optimization Techniques Open access

Hung-Hsu Chou, Carsten Gieshoff, Johannes Maly, Holger Rauhut

5

citations
Gradient Descent for Deep Matrix Factorization: Dynamics and Implicit Bias towards Low Rank
2020 arXiv (Cornell University) preprint Computer Science Stochastic Gradient Optimization Techniques Open access

Hung-Hsu Chou, Carsten Gieshoff, Johannes Maly, Holger Rauhut

2

citations
Structured Layerwise Pruning via Extended-Interval Activation- and Derivative-Based Significance Measures
2025 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) conference-paper Computer Science Advanced Neural Network Applications

Mansoor Ahmad, Sher Afghan, Uwe Naumann, Ekkapot Charoenwanit

0

citations
Towards Continuous Mathematical Models for the Analysis of Classes of Deep Neural Networks
2024 Springer eBooks book-chapter Computer Science Neural Networks and Applications

Angela Kunoth, Mathias Oster, Reinhold Schneider

0

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

1

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
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

17 results