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Papers matching “kernel functions” 45

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A comparison study of supervised learning techniques for the approximation of high dimensional functions and feedback control
2024 arXiv (Cornell University) preprint Computer Science Neural Networks and Applications Open access

Mathias Oster, Luca Saluzzi, Tizian Wenzel

1

citations
High Dimensional Bayesian Optimization with Kernel Principal Component Analysis
2022 arXiv (Cornell University) preprint Decision Sciences Advanced Bandit Algorithms Research Open access

Kirill Antonov, Elena Raponi, Hao Wang, Carola Doerr

2

citations
Lipschitz and Hölder Continuity in Reproducing Kernel Hilbert Spaces
2023 RWTH Publications (RWTH Aachen) preprint Mathematics Advanced Banach Space Theory Open access

Christian Fiedler

0

citations
Points of non-linearity of functions generated by random neural networks
2023 arXiv (Cornell University) preprint Computer Science Neural Networks and Applications Open access

0

citations
Unfolding recurrence by Green's functions for optimized reservoir computing
2020 arXiv (Cornell University) preprint Computer Science Neural Networks and Reservoir Computing Open access

Sandra Nestler, Christian Keup, David Dahmen, Matthieu Gilson, Holger Rauhut, Moritz Helias

0

citations
On genetic programming representations and fitness functions for interpretable dimensionality reduction
2022 arXiv (Cornell University) preprint Computer Science Evolutionary Algorithms and Applications Open access

Thomas Uriot, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman

3

citations
Deep kernel video approximation for unsupervised action segmentation
2026 arXiv (Cornell University) preprint Computer Science Human Pose and Action Recognition Open access

Silvia L. Pintea, Jouke Dijkstra

0

citations
Deep kernel video approximation for unsupervised action segmentation
2026 arXiv (Cornell University) preprint Computer Science Human Pose and Action Recognition Open access

Silvia L. Pintea, Jouke Dijkstra

0

citations
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Data
2022 arXiv (Cornell University) preprint Physics and Astronomy Model Reduction and Neural Networks Open access

Mathis Bode, Michael Gauding, Dominik Goeb, Tobias Falkenstein, Heinz G. Pitsch

0

citations
Universality and kernel-adaptive training for classically trained, quantum-deployed generative models
2025 arXiv (Cornell University) preprint Computer Science Quantum Computing Algorithms and Architecture Open access

Andrii Kurkin, Susanne Pielawa, Hao Wang, Vedran Dunjko

0

citations
The NNLO gluon beam function for jet-veto resummation
2024 arXiv (Cornell University) preprint Physics and Astronomy Particle physics theoretical and experimental studies Open access

Guido Bell, Kevin Brune, Goutam Das, Ding Yu Shao, Marcel Wald

0

citations
Dynamics of neural scaling laws in random feature regression with powerlaw-distributed kernel eigenvalues
2026 arXiv (Cornell University) preprint Computer Science Gaussian Processes and Bayesian Inference Open access

Jakob Kramp, Javed Lindner, Moritz Helias

0

citations
Automatic nonlinear MPC approximation with closed-loop guarantees
2023 arXiv (Cornell University) preprint Engineering Advanced Control Systems Optimization Open access

Abdullah Tokmak, Christian Fiedler, Melanie N. Zeilinger, Sebastian Trimpe, Johannes Köhler

1

citations
Critical feature learning in deep neural networks
2024 arXiv (Cornell University) preprint Computer Science Gaussian Processes and Bayesian Inference Open access

Javed Lindner, David Dahmen, Zohar Ringel, M. Krämer, Moritz Helias

0

citations
Surfacing Estimation Uncertainty in the Decay Parameters of Hawkes Processes with Exponential Kernels
2021 arXiv (Cornell University) preprint Mathematics Point processes and geometric inequalities Open access

Tiago Santos, Florian Lemmerich, Denis Helić

0

citations
Discrete signaling mediates chaotic regularization in recurrent neural networks
2026 arXiv (Cornell University) preprint Neuroscience Neural dynamics and brain function Open access

Jan Bauer, Christian Keup, Jonathan Kadmon, Moritz Helias

0

citations
Convolutional Neural Networks as 2-D systems
2023 arXiv (Cornell University) preprint Computer Science Neural Networks and Applications Open access

Dennis Gramlich, Patricia Pauli, Carsten W. Scherer, Frank Allgöwer, Christian Ebenbauer

3

citations
Agentic Bayesian Optimization through Surrogate-Augmented Autoresearch
2026 arXiv (Cornell University) preprint Decision Sciences Advanced Bandit Algorithms Research Open access

Paul Brunzema, Louis C. Tiao, Nhat Le, Kevin De Angeli, Yao Xuan, Djordje Gligorijevic

0

citations
A unified weighting framework for evaluating nearest neighbour classification
2023 arXiv (Cornell University) preprint Computer Science Machine Learning and Data Classification Open access

Oliver Urs Lenz, Henri Bollaert, Chris Cornelis

0

citations
Testing for Stable Intervals in Non-Stationary Time Series
2026 arXiv (Cornell University) preprint Mathematics Statistical Methods and Inference Open access

Florian Heinrichs

0

citations
Rotation Equivariant Operators for Machine Learning on Scalar and Vector Fields
2021 arXiv (Cornell University) preprint Physics and Astronomy Model Reduction and Neural Networks Open access

Venkat Viswanathan

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
On Foundation Models for Dynamical Systems from Purely Synthetic Data
2024 arXiv (Cornell University) preprint Computer Science Reinforcement Learning in Robotics Open access

Andrés Felipe Posada-Moreno, Friedrich Solowjow, Sebastian Trimpe

1

citations
Adaptation using spatially distributed Gaussian Processes
2023 arXiv (Cornell University) preprint Computer Science Gaussian Processes and Bayesian Inference Open access

Botond Szabó, Amine Hadji, Aad van der Vaart

0

citations
Optimal recovery and uncertainty quantification for distributed Gaussian process regression
2022 arXiv (Cornell University) preprint Computer Science Gaussian Processes and Bayesian Inference Open access

Amine Hadji, Tammo Hesselink, Botond Szabó

3

citations