Imagen de Portada

Comparing statistical and deep learning techniques for parameter estimation of continuous-time stochastic differentiable equations

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Sankoh, Aroon, Wickerhauser, Victor
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Probability
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Fisher information dissipation for time inhomogeneous stochastic differential equations
por: Feng, Qi, et al.
Publicado: (2024)

The generator gradient estimator is an adjoint state method for stochastic differential equations
por: Badolle, Quentin, et al.
Publicado: (2024)

Deep learning based numerical approximation algorithms for stochastic partial differential equations
por: Beck, Christian, et al.
Publicado: (2020)

Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"
por: Paulin, Daniel, et al.
Publicado: (2024)

Effective continuous equations for adaptive SGD: a stochastic analysis view
por: Callisti, Luca, et al.
Publicado: (2025)

Neural stochastic Volterra equations: learning path-dependent dynamics
por: Bergerhausen, Martin, et al.
Publicado: (2024)

Active operator learning with predictive uncertainty quantification for partial differential equations
por: Winovich, Nick, et al.
Publicado: (2025)

Space-time deep neural network approximations for high-dimensional partial differential equations
por: Hornung, Fabian, et al.
Publicado: (2020)

Solving stochastic partial differential equations using neural networks in the Wiener chaos expansion
por: Neufeld, Ariel, et al.
Publicado: (2024)

Extraction of informative statistical features in the problem of forecasting time series generated by It{ô}-type processes
por: Korolev, Victor, et al.
Publicado: (2026)

Data-driven approximation of transfer operators for mean-field stochastic differential equations
por: Ioannou, Eirini, et al.
Publicado: (2025)

Approximating G(t)/GI/1 queues with deep learning
por: Sherzer, Eliran, et al.
Publicado: (2024)

Comparative study of machine learning and statistical methods for automatic identification and quantification in γ-ray spectrometry
por: Phan, Dinh Triem, et al.
Publicado: (2025)

Uncertainty estimation via ensembles of deep learning models and dropout layers for seismic traces
por: Messuti, Giovanni, et al.
Publicado: (2024)

High Rank Path Development: an approach of learning the filtration of stochastic processes
por: Tao, Jiajie, et al.
Publicado: (2024)

On Bellman equations for continuous-time policy evaluation I: discretization and approximation
por: Mou, Wenlong, et al.
Publicado: (2024)

Predicting path-dependent processes by deep learning
por: Zheng, Xudong, et al.
Publicado: (2024)

Lipschitz continuity in the Hurst parameter of functionals of stochastic differential equations driven by a fractional Brownian motion
por: Richard, Alexandre, et al.
Publicado: (2016)

Off-the-grid learning of mixtures from a continuous dictionary
por: Butucea, Cristina, et al.
Publicado: (2022)

Score matching for bridges without learning time-reversals
por: Baker, Elizabeth L., et al.
Publicado: (2024)

Efficient estimation of jump parameters for stochastic differential equations driven by L{é}vy processes
por: Bayraktar, Elise, et al.
Publicado: (2025)

Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models
por: Winkler, Ludwig, et al.
Publicado: (2024)

A distance function for stochastic matrices
por: Lee, Antony R., et al.
Publicado: (2024)

Simultaneous off-the-grid learning of mixtures issued from a continuous dictionary
por: Butucea, Cristina, et al.
Publicado: (2022)

Long-time asymptotics of noisy SVGD outside the population limit
por: Priser, Victor, et al.
Publicado: (2024)

Extreme Value theory and Poisson statistics for discrete time samplings of stochastic differential equations
por: Flandoli, F., et al.
Publicado: (2023)

Multilevel Picard approximations and deep neural networks with ReLU, leaky ReLU, and softplus activation overcome the curse of dimensionality when approximating semilinear parabolic partial differential equations in $L^p$-sense
por: Neufeld, Ariel, et al.
Publicado: (2024)

Finite sample bounds for barycenter estimation in geodesic spaces
por: Brunel, Victor-Emmanuel, et al.
Publicado: (2025)

Small coresets via negative dependence: DPPs, linear statistics, and concentration
por: Bardenet, Rémi, et al.
Publicado: (2024)

Towards certification: A complete statistical validation pipeline for supervised learning in industry
por: Lacasa, Lucas, et al.
Publicado: (2024)

Spectral alignment of stochastic gradient descent for high-dimensional classification tasks
por: Arous, Gerard Ben, et al.
Publicado: (2023)

Tunable correlation retention: A statistical method for generating synthetic data
por: Jävergård, Nicklas, et al.
Publicado: (2024)

Large-width functional asymptotics for deep Gaussian neural networks
por: Bracale, Daniele, et al.
Publicado: (2021)

A simple algorithm for output range analysis for deep neural networks
por: Rojas, Helder, et al.
Publicado: (2024)

Exploring how deep learning decodes anomalous diffusion via Grad-CAM
por: Bae, Jaeyong, et al.
Publicado: (2024)

An information field theory approach to Bayesian state and parameter estimation in dynamical systems
por: Hao, Kairui, et al.
Publicado: (2023)

Non-asymptotic statistical test of the diffusion coefficient of stochastic differential equations
por: Melnykova, Anna, et al.
Publicado: (2023)

Symplectic techniques for stochastic differential equations on reductive Lie groups with applications to Langevin diffusions
por: Luesink, Erwin, et al.
Publicado: (2025)

Neural posterior estimation for scalable and accurate inverse parameter inference in Li-ion batteries
por: Hassanaly, Malik, et al.
Publicado: (2026)

Binary stochasticity enabled highly efficient neuromorphic deep learning achieves better-than-software accuracy
por: Li, Yang, et al.
Publicado: (2023)