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Papers matching “neural network approximation” 240

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Fine-Tuning of Neural Network Approximate MPC without Retraining via Bayesian Optimization
2025 arXiv (Cornell University) preprint Engineering Advanced Control Systems Optimization Open access

Henrik Hose, Paul Brunzema, Alexander von Rohr, Alexander Gräfe, Angela P. Schoellig, Sebastian Trimpe

0

citations
A theory of data variability in Neural Network Bayesian inference
2023 arXiv (Cornell University) preprint Computer Science Gaussian Processes and Bayesian Inference Open access

Javed Lindner, David Dahmen, Michael Krämer, Moritz Helias

0

citations
Data variability in neural network Bayesian inference
2025 Physical Review Research article Computer Science Gaussian Processes and Bayesian Inference Open access

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
A mean-field toolbox for spiking neuronal network model analysis
2021 bioRxiv (Cold Spring Harbor Laboratory) preprint Neuroscience Neural dynamics and brain function Open access

Moritz Layer, Johanna Senk, Simon Essink, Alexander van Meegen, Hannah Bos, Moritz Helias

0

citations
Approximate Nonlinear Model Predictive Control With Safety-Augmented Neural Networks
2025 IEEE Transactions on Control Systems Technology article Engineering Advanced Control Systems Optimization Open access

Henrik Hose, Johannes Köhler, Melanie N. Zeilinger, Sebastian Trimpe

13

citations
The edge of chaos: quantum field theory and deep neural networks
2022 SciPost Physics article Physics and Astronomy Theoretical and Computational Physics Open access

Kevin T. Grosvenor, Ro Jefferson

30

citations
The edge of chaos: quantum field theory and deep neural networks
2021 arXiv (Cornell University) preprint Physics and Astronomy Theoretical and Computational Physics Open access

Kevin T. Grosvenor, Ro Jefferson

0

citations
RONAALP: Reduced-Order Nonlinear Approximation with Active Learning Procedure
2023 arXiv (Cornell University) preprint Physics and Astronomy Model Reduction and Neural Networks Open access

Clément Scherding, Denis Sipp, Peter J. Schmid, Taraneh Sayadi

3

citations
Controllability of continuous networks and a kernel-based learning approximation
2024 arXiv (Cornell University) preprint Physics and Astronomy Model Reduction and Neural Networks Open access

Michaël Herty, Chiara Segala, Giuseppe Visconti

0

citations
Mini-Workshop: Nonlinear Approximation of High-dimensional Functions in Scientific Computing
2024 Oberwolfach Reports article Mathematics Mathematical Approximation and Integration Open access

Mathias Oster, Janina Schütte, Philipp Trunschke

0

citations
Uncovering a Universal Abstract Algorithm for Modular Addition in Neural Networks
2025 neural information processing systems conference-paper Computer Science Neural Networks and Applications Open access

Gavin McCracken, Gabriela Moisescu-Pareja, Vincent Létourneau, Doina Precup

0

citations
Characterizing Neural Manifolds' Properties and Curvatures using Normalizing Flows
2026 PRX Life article Neuroscience Neural dynamics and brain function Open access

Peter Bouss, Sandra Nestler, Kirsten Fischer, Claudia Merger, Lars Schutzeichel, Simon Musall, +2 more

0

citations
Automated Design of Linear Bounding Functions for Sigmoidal Nonlinearities in Neural Networks
2024 arXiv (Cornell University) preprint Computer Science Adversarial Robustness in Machine Learning Open access

Xiyue Zhang, Holger H. Hoos, Marta Kwiatkowska, Jan N. van Rijn

0

citations
Code Repository for the Paper "Characterizing Neural Manifolds' Properties and Curvatures using Normalizing Flows" (PRX Life, 2026)
2026 Zenodo (CERN European Organization for Nuclear Research) article Neuroscience Neural dynamics and brain function Open access

Peter Bouss, Sandra Nestler, Kirsten Fischer, Claudia Merger, Lars Schutzeichel, Simon Musall, +2 more

1

citations
Code Repository for the Paper "Characterizing Neural Manifolds' Properties and Curvatures using Normalizing Flows" (PRX Life, 2026)
2026 Zenodo (CERN European Organization for Nuclear Research) article Neuroscience Neural dynamics and brain function Open access

Peter Bouss, Sandra Nestler, Kirsten Fischer, Claudia Merger, Lars Schutzeichel, Simon Musall, +2 more

0

citations
Fine-grained Expressivity of Graph Neural Networks
2023 arXiv (Cornell University) preprint Computer Science Advanced Graph Neural Networks Open access

Jan Böker, Ron Levie, Ningyuan Teresa Huang, Soledad Villar

4

citations
Non-Asymptotic Analysis of Projected Gradient Descent for Physics-Informed Neural Networks
2025 arXiv (Cornell University) preprint Physics and Astronomy Model Reduction and Neural Networks Open access

Jonas Nießen

0

citations
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
NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models
2022 Frontiers in Neuroinformatics article Neuroscience Neural dynamics and brain function Open access

Moritz Layer, Johanna Senk, Simon Essink, Alexander van Meegen, Hannah Bos, Moritz Helias

12

citations
A generalized framework of neural networks for Hamiltonian systems
2024 Journal of Computational Physics article Physics and Astronomy Model Reduction and Neural Networks Open access

Philipp Horn, Veronica Saz Ulibarrena, Barry Koren, Simon Portegies Zwart

11

citations
Linking network- and neuron-level correlations by renormalized field theory
2024 Physical Review Research article Neuroscience Neural dynamics and brain function Open access

M.I. Dick, Alexander van Meegen, Moritz Helias

8

citations
Linking Network and Neuron-level Correlations by Renormalized Field Theory
2023 arXiv (Cornell University) preprint Neuroscience Neural dynamics and brain function Open access

Michael J. Dick, Alexander van Meegen, Moritz Helias

0

citations
Machine-Learning Based Approximation of the Hierarchical Model Predictive Control of Multi-Use PV-Battery Systems in Non-Residential Buildings
2023 International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS) conference-paper Engineering Building Energy and Comfort Optimization Open access

Laura Maier, Sönke Quast, Dominik Hering, Dirk Müller

1

citations
Discovering uncertainty: Bayesian constitutive artificial neural networks
2024 Computer Methods in Applied Mechanics and Engineering article Computer Science Gaussian Processes and Bayesian Inference Open access

Kevin Linka, Gerhard A. Holzapfel, Ellen Kuhl

37

citations