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Papers matching “deep matrix factorization” 9

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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
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
Deep Nonnegative Matrix Factorization with Joint Global and Local Structure Preservation
2024 Expert Systems with Applications article Computer Science Face and Expression Recognition

Farid Saberi-Movahed, B. Biswas, Prayag Tiwari, Jens Lehmann

41

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
Convergence of gradient descent for learning linear neural networks
2024 Advances in Continuous and Discrete Models article Computer Science Stochastic Gradient Optimization Techniques Open access

Gabin Maxime Nguegnang, Holger Rauhut, Ulrich Terstiege

10

citations
Convergence of gradient descent for learning linear neural networks
2021 arXiv (Cornell University) preprint Computer Science Stochastic Gradient Optimization Techniques Open access

Gabin Maxime Nguegnang, Holger Rauhut, Ulrich Terstiege

1

citations
Norm Loss: An efficient yet effective regularization method for deep neural networks
2021 Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition conference-paper Computer Science Advanced Neural Network Applications

Sebastian Schmitt, Thomas Bäck, Wei Chen, Michael S. Lew

3

citations
Preprint: Norm Loss: An efficient yet effective regularization method for deep neural networks
2021 arXiv (Cornell University) preprint Computer Science Advanced Neural Network Applications Open access

Sebastian Schmitt, Thomas Bäck, Wei Chen, Michael S. Lew

1

citations

9 results