Saved in:
| Main Authors: | Douglas, Owen, Kammonen, Aku, Pandey, Anamika, Tempone, Raúl |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.15442 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive Random Fourier Features
by: Kammonen, Aku, et al.
Published: (2024)
by: Kammonen, Aku, et al.
Published: (2024)
Adaptive Random Fourier Features Training Stabilized By Resampling With Applications in Image Regression
by: Kammonen, Aku, et al.
Published: (2024)
by: Kammonen, Aku, et al.
Published: (2024)
Convergence for adaptive resampling of random Fourier features
by: Huang, Xin, et al.
Published: (2025)
by: Huang, Xin, et al.
Published: (2025)
Residual Multi-Fidelity Neural Network Computing
by: Davis, Owen, et al.
Published: (2023)
by: Davis, Owen, et al.
Published: (2023)
Neural Collapse Dynamics: Depth, Activation, Regularisation, and Feature Norm Threshold
by: Rupa, Anamika Paul
Published: (2026)
by: Rupa, Anamika Paul
Published: (2026)
Deep NURBS -- Admissible Physics-informed Neural Networks
by: Saidaoui, Hamed, et al.
Published: (2022)
by: Saidaoui, Hamed, et al.
Published: (2022)
Solving Partial Differential Equations with Random Feature Models
by: Liao, Chunyang
Published: (2024)
by: Liao, Chunyang
Published: (2024)
Random Fourier Signature Features
by: Toth, Csaba, et al.
Published: (2023)
by: Toth, Csaba, et al.
Published: (2023)
Neural Structure Learning with Stochastic Differential Equations
by: Wang, Benjie, et al.
Published: (2023)
by: Wang, Benjie, et al.
Published: (2023)
Random Controlled Differential Equations
by: Piatti, Francesco, et al.
Published: (2025)
by: Piatti, Francesco, et al.
Published: (2025)
End-to-end Kernel Learning via Generative Random Fourier Features
by: Fang, Kun, et al.
Published: (2020)
by: Fang, Kun, et al.
Published: (2020)
ANOVA-boosting for Random Fourier Features
by: Potts, Daniel, et al.
Published: (2024)
by: Potts, Daniel, et al.
Published: (2024)
In-Context Learning of Stochastic Differential Equations with Foundation Inference Models
by: Seifner, Patrick, et al.
Published: (2025)
by: Seifner, Patrick, et al.
Published: (2025)
Modeling Time Series Dynamics with Fourier Ordinary Differential Equations
by: Guo, Muhao, et al.
Published: (2025)
by: Guo, Muhao, et al.
Published: (2025)
Towards Identifiability of Interventional Stochastic Differential Equations
by: Zweig, Aaron, et al.
Published: (2025)
by: Zweig, Aaron, et al.
Published: (2025)
Invertible Kernel PCA with Random Fourier Features
by: Gedon, Daniel, et al.
Published: (2023)
by: Gedon, Daniel, et al.
Published: (2023)
Filtered Markovian Projection: Dimensionality Reduction in Filtering for Stochastic Reaction Networks
by: Hammouda, Chiheb Ben, et al.
Published: (2025)
by: Hammouda, Chiheb Ben, et al.
Published: (2025)
Learning Controlled Stochastic Differential Equations
by: Brogat-Motte, Luc, et al.
Published: (2024)
by: Brogat-Motte, Luc, et al.
Published: (2024)
Variational Neural Stochastic Differential Equations with Change Points
by: El-Laham, Yousef, et al.
Published: (2024)
by: El-Laham, Yousef, et al.
Published: (2024)
Physics-embedded Fourier Neural Network for Partial Differential Equations
by: Xu, Qingsong, et al.
Published: (2024)
by: Xu, Qingsong, et al.
Published: (2024)
Component Fourier Neural Operator for Singularly Perturbed Differential Equations
by: Li, Ye, et al.
Published: (2024)
by: Li, Ye, et al.
Published: (2024)
Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent
by: Schertzer, Adrien, et al.
Published: (2024)
by: Schertzer, Adrien, et al.
Published: (2024)
Embedded Variational Neural Stochastic Differential Equations for Learning Heterogeneous Dynamics
by: Samota, Sandeep Kumar, et al.
Published: (2026)
by: Samota, Sandeep Kumar, et al.
Published: (2026)
Adaptive RKHS Fourier Features for Compositional Gaussian Process Models
by: Shi, Xinxing, et al.
Published: (2024)
by: Shi, Xinxing, et al.
Published: (2024)
Cluster-Based Generalized Additive Models Informed by Random Fourier Features
by: Huang, Xin, et al.
Published: (2025)
by: Huang, Xin, et al.
Published: (2025)
Hierarchical Stochastic Differential Equation Models for Latent Manifold Learning in Neural Time Series
by: Rajaei, Pedram, et al.
Published: (2025)
by: Rajaei, Pedram, et al.
Published: (2025)
HGAN-SDEs: Learning Neural Stochastic Differential Equations with Hermite-Guided Adversarial Training
by: Xu, Yuanjian, et al.
Published: (2025)
by: Xu, Yuanjian, et al.
Published: (2025)
Plastic Learning with Deep Fourier Features
by: Lewandowski, Alex, et al.
Published: (2024)
by: Lewandowski, Alex, et al.
Published: (2024)
Sampling in High-Dimensions using Stochastic Interpolants and Forward-Backward Stochastic Differential Equations
by: George, Anand Jerry, et al.
Published: (2025)
by: George, Anand Jerry, et al.
Published: (2025)
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations
by: Xu, Jian, et al.
Published: (2024)
by: Xu, Jian, et al.
Published: (2024)
A Complete Decomposition of Stochastic Differential Equations
by: Duffield, Samuel
Published: (2026)
by: Duffield, Samuel
Published: (2026)
Neural Laplace for learning Stochastic Differential Equations
by: Carrel, Adrien
Published: (2024)
by: Carrel, Adrien
Published: (2024)
Drift Estimation for Stochastic Differential Equations with Denoising Diffusion Models
by: Costa, Marcos Tapia, et al.
Published: (2026)
by: Costa, Marcos Tapia, et al.
Published: (2026)
Improving the Noise Estimation of Latent Neural Stochastic Differential Equations
by: Heck, Linus, et al.
Published: (2024)
by: Heck, Linus, et al.
Published: (2024)
Expanding the Chaos: Neural Operator for Stochastic (Partial) Differential Equations
by: Shi, Dai, et al.
Published: (2026)
by: Shi, Dai, et al.
Published: (2026)
Differentially Private Random Feature Model
by: Liao, Chunyang, et al.
Published: (2024)
by: Liao, Chunyang, et al.
Published: (2024)
DInf-Grid: A Neural Differential Equation Solver with Differentiable Feature Grids
by: Kairanda, Navami, et al.
Published: (2026)
by: Kairanda, Navami, et al.
Published: (2026)
Semantic Editing with Coupled Stochastic Differential Equations
by: Zhang, Jianxin, et al.
Published: (2025)
by: Zhang, Jianxin, et al.
Published: (2025)
RFFNet: Large-Scale Interpretable Kernel Methods via Random Fourier Features
by: Otto, Mateus P., et al.
Published: (2022)
by: Otto, Mateus P., et al.
Published: (2022)
Approximation to Deep Q-Network by Stochastic Delay Differential Equations
by: Lu, Jianya, et al.
Published: (2025)
by: Lu, Jianya, et al.
Published: (2025)
Similar Items
-
Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive Random Fourier Features
by: Kammonen, Aku, et al.
Published: (2024) -
Adaptive Random Fourier Features Training Stabilized By Resampling With Applications in Image Regression
by: Kammonen, Aku, et al.
Published: (2024) -
Convergence for adaptive resampling of random Fourier features
by: Huang, Xin, et al.
Published: (2025) -
Residual Multi-Fidelity Neural Network Computing
by: Davis, Owen, et al.
Published: (2023) -
Neural Collapse Dynamics: Depth, Activation, Regularisation, and Feature Norm Threshold
by: Rupa, Anamika Paul
Published: (2026)