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Papers matching “stochastic gradient descent” 25

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Stochastic Gradient Descent with Momentum: Analysis and Synthesis via Integral Quadratic Constraints
2026 arXiv (Cornell University) preprint Computer Science Stochastic Gradient Optimization Techniques Open access

Dennis Gramlich, Carsten W. Scherer, Christian Ebenbauer

0

citations
Deep Relaxation of Controlled Stochastic Gradient Descent via Singular Perturbations
2024 SIAM Journal on Control and Optimization article Computer Science Advanced Mathematical Modeling in Engineering Open access

Martino Bardi, Hicham Kouhkouh

4

citations
A Poisson Jump-driven SDE Approach to Distributed Gradient Descent with Sparse Communication
2025 arXiv (Cornell University) preprint Computer Science Distributed Control Multi-Agent Systems Open access

John Paul Strachan, Christian Ebenbauer

0

citations
Equating quantum imaginary time evolution, Riemannian gradient flows, and stochastic implementations
2026 Physical Review Research article Computer Science Quantum Computing Algorithms and Architecture Open access

Nathan A. McMahon, Mahum Pervez, Christian Arenz

4

citations
Equating quantum imaginary time evolution, Riemannian gradient flows, and stochastic implementations
2025 arXiv (Cornell University) preprint Computer Science Quantum Computing Algorithms and Architecture Open access

Nathan A. McMahon, Mahum Pervez, Christian Arenz

0

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
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
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
Finite-Time Analysis of Gradient Descent for Shallow Transformers
2026 arXiv (Cornell University) preprint Computer Science Stochastic Gradient Optimization Techniques Open access

Enes Arda, Semih Çaycı, Atilla Eryılmaz

0

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
Convergence guarantees for forward gradient descent in the linear regression model
2024 Journal of Statistical Planning and Inference article Computer Science Stochastic Gradient Optimization Techniques Open access

Thijs Bos, Johannes Schmidt-Hieber

0

citations
Riemannian gradient descent-based quantum algorithms for ground state preparation with guarantees
2025 arXiv (Cornell University) preprint Computer Science Quantum Computing Algorithms and Architecture Open access

Mahum Pervez, Ariq Haqq, Nathan A. McMahon, Christian Arenz

0

citations
Randomized HyperSteiner: A Stochastic Delaunay Triangulation Heuristic for the Hyperbolic Steiner Minimal Tree
2025 arXiv (Cornell University) preprint Computer Science Computational Geometry and Mesh Generation Open access

Aniss Aiman Medbouhi, Alejandro García-Castellanos, Giovanni Luca Marchetti, Daniël M. Pelt, Erik J. Bekkers, Danica Kragić

0

citations
Resource frugal optimizer for quantum machine learning
2023 Quantum Science and Technology article Computer Science Quantum Computing Algorithms and Architecture Open access

Charles Moussa, Max Hunter Gordon, Michał Bączyk, Marco Cerezo, Łukasz Cincio, Patrick J. Coles

9

citations
Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive Random Fourier Features
2024 arXiv (Cornell University) preprint Computer Science Stochastic Gradient Optimization Techniques Open access

Aku Kammonen, Raul F. Tempone

0

citations
The pretraining domain outweighs the training objective in setting the privacy-utility trade-off of differentially private medical image analysis
2026 arXiv (Cornell University) preprint Computer Science Privacy-Preserving Technologies in Data Open access

Soroosh Tayebi Arasteh, Mina Farajiamiri, Mahshad Lotfinia, Behrus Puladi, Jonas Bienzeisler, Mohamed Alhaskir, +4 more

0

citations
Focal Sampling: SGD biased towards early important samples for efficient image classification with augmentation selection
2025 Knowledge and Information Systems article Computer Science Machine Learning and Data Classification Open access

Alessio Quercia, Abigail Morrison, Hanno Scharr, Ira Assent

0

citations
Automatic Learning of Subword Dependent Model Scales
2021 arXiv (Cornell University) preprint Computer Science Speech Recognition and Synthesis Open access

Wilfried Michel, Mohammad Zeineldeen, Ralf Schlüter, Hermann Ney

0

citations
Automatic Learning of Subword Dependent Model Scales
2022 Interspeech (USB) conference-paper Computer Science Speech Recognition and Synthesis Open access

Felix Meyer, Wilfried Michel, Mohammad Zeineldeen, Ralf Schlüter, Hermann Ney

0

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
One Model, Any CSP: Graph Neural Networks as Fast Global Search Heuristics for Constraint Satisfaction
2023 International Joint Conference on Artificial Intelligence conference-paper Computer Science Constraint Satisfaction and Optimization Open access

Jan Tönshoff, Berke Kisin, Jakob Lindner, Martin Grohe

15

citations
One Model, Any CSP: Graph Neural Networks as Fast Global Search Heuristics for Constraint Satisfaction
2022 arXiv (Cornell University) preprint Computer Science Constraint Satisfaction and Optimization Open access

Jan Tönshoff, Berke Kisin, Jakob Lindner, Martin Grohe

1

citations
Faster Convergence for Transformer Fine-tuning with Line Search Methods
2023 conference-paper Computer Science Stochastic Gradient Optimization Techniques Open access

Philip Kenneweg, Leonardo Galli, Tristan Kenneweg, Barbara Hammer

2

citations
Privacy-Preserving Deep Learning for the Detection of Protected Health Information in Real-World Data: Comparative Evaluation
2020 JMIR Formative Research article Computer Science Privacy-Preserving Technologies in Data Open access

Sven Festag, Cord Spreckelsen

12

citations

25 results