Cover Image

A data-driven Fourier-mixture neural-network method for density estimation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Dang, Duy-Minh, Entoma, Volter
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Computational Finance 65D15, 62G07, 60E10, 68T07, 41A30
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Convergence of Neural Network Policies for Risk--Reward Optimization
by: Chen, Chang, et al.
Published: (2026)

LDP for the covariance process in fully connected neural networks
by: Andreis, Luisa, et al.
Published: (2025)

Hyperbolic trigonometric functions as approximation kernels and their properties II: Wavelets
by: Buhmann, M., et al.
Published: (2025)

High-dimensional Bayesian filtering through deep density approximation
by: Bågmark, Kasper, et al.
Published: (2025)

Nonlinear filtering based on density approximation and deep BSDE prediction
by: Bågmark, Kasper, et al.
Published: (2025)

An energy-based deep splitting method for the nonlinear filtering problem
by: Bågmark, Kasper, et al.
Published: (2022)

Error analysis for empirical risk minimization over clipped ReLU networks in solving linear Kolmogorov partial differential equations
by: Xiao, Jichang, et al.
Published: (2023)

Ridge Kernel Averaging and Uniform Approximation
by: Tian, James
Published: (2025)

Equidistribution-based training of Free Knot Splines and ReLU Neural Networks
by: Appella, Simone, et al.
Published: (2024)

A convergent scheme for the Bayesian filtering problem based on the Fokker--Planck equation and deep splitting
by: Bågmark, Kasper, et al.
Published: (2024)

Spatio-temporal probabilistic forecast using MMAF-guided learning
by: Bardi, Leonardo, et al.
Published: (2026)

Improving Matrix Exponential for Generative AI Flows: A Taylor-Based Approach Beyond Paterson--Stockmeyer
by: Sastre, Jorge, et al.
Published: (2025)

A Construction of $C^{r}$ Conforming Finite Elements on the Alfeld Split in Any Dimension
by: Lin, Ting, et al.
Published: (2026)

A new architecture of high-order deep neural networks that learn martingales
by: Ninomiya, Syoiti, et al.
Published: (2025)

An adaptive adjoint-oriented neural network for solving parametric optimal control problems with singularities
by: Yuan, Zikang, et al.
Published: (2025)

Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data
by: Gyger, Tim, et al.
Published: (2024)

Certified and accurate computation of function space norms of deep neural networks
by: Gründler, Johannes, et al.
Published: (2026)

Measure Concentration for Compound Poisson Distributions
by: Kontoyiannis, I., et al.
Published: (2005)

Deep Learning Based on Randomized Quasi-Monte Carlo Method for Solving Linear Kolmogorov Partial Differential Equation
by: Xiao, Jichang, et al.
Published: (2023)

Trustworthy AI in numerics: On verification algorithms for neural network-based PDE solvers
by: Haugen, Emil, et al.
Published: (2025)

Error analysis for learning fractional stochastic differential equations with applications in neural approximations
by: Dehshiri, Mahdi, et al.
Published: (2026)

Estimation of accuracy and reliability of models of $φ$-sub-Gaussian stochastic processes in $C(T)$ spaces
by: Mokliachuk, Oleksandr
Published: (2025)

Modeling of stochastic processes in $L_p(T)$ using orthogonal polynomials
by: Mokliachuk, Oleksandr
Published: (2025)

Neural Network Convergence for Variational Inequalities
by: Zhao, Yun, et al.
Published: (2025)

Asymptotic stability properties and a priori bounds for Adam and other gradient descent optimization methods
by: Dereich, Steffen, et al.
Published: (2025)

Complexity of exact sampling of the first passage of a stable subordinator
by: Chi, Zhiyi
Published: (2025)

A forward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations
by: Kapllani, Lorenc, et al.
Published: (2024)

A backward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations
by: Kapllani, Lorenc, et al.
Published: (2024)

On the minimax optimality of Flow Matching through the connection to kernel density estimation
by: Kunkel, Lea, et al.
Published: (2025)

Solving Approximation Tasks with Greedy Deep Kernel Methods
by: Klink, Marian, et al.
Published: (2025)

Biasing with an independent increment: Gaussian approximations and proximity of Poisson mixtures
by: Daly, Fraser
Published: (2025)

A neuron-wise subspace correction method for the finite neuron method
by: Park, Jongho, et al.
Published: (2022)

SUPN: Shallow Universal Polynomial Networks
by: Morrow, Zachary, et al.
Published: (2025)

Universal Shuffle Asymptotics, Part III: Dominant-Block Quotient Geometry and Hybrid Gaussian--Compound-Poisson Limits in Finite-Alphabet Shuffle Privacy
by: Shvets, Alex
Published: (2026)

Weights initialization of neural networks for function approximation
by: Hu, Xinwen, et al.
Published: (2025)

Spectral complexity of deep neural networks
by: Di Lillo, Simmaco, et al.
Published: (2024)

Learning where to learn: Training data distribution optimization for scientific machine learning
by: Guerra, Nicolas, et al.
Published: (2025)

Neural enrichment finite element method: A hybrid framework for problems with strong oscillations or interface problems
by: Guo, Shihan, et al.
Published: (2026)

In almost all shallow analytic neural network optimization landscapes, efficient minimizers have strongly convex neighborhoods
by: Benning, Felix, et al.
Published: (2025)

A neural network method for scalar conservation laws with convergence rates for shock-wave solutions
by: Cao, Jiachuan, et al.
Published: (2026)