Copertina

Neural network parametrized level sets for image segmentation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Scherzer, Otmar, Shi, Cong, Vu, Thi Lan Nhi
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Numerical Analysis
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Neural operators for solving nonlinear inverse problems
di: Scherzer, Otmar, et al.
Pubblicazione: (2025)

Addendum on data driven regularization by projection
di: Hanke, Martin, et al.
Pubblicazione: (2025)

Quadratic neural networks for solving inverse problems
di: Frischauf, Leon, et al.
Pubblicazione: (2023)

Classification with neural networks with quadratic decision functions
di: Frischauf, Leon, et al.
Pubblicazione: (2024)

Raster Scan Diffraction Tomography
di: Elbau, Peter, et al.
Pubblicazione: (2026)

Spectral Function Space Learning and Numerical Linear Algebra Networks for Solving Linear Inverse Problems
di: Aspri, Andrea, et al.
Pubblicazione: (2024)

Diffraction Tomography for a Generalized Incident Field
di: Kirisits, Clemens, et al.
Pubblicazione: (2024)

A multi-level algorithm for the solution of moment problems
di: Scherzer, Otmar, et al.
Pubblicazione: (1999)

Vertex characterization via second-order topological derivatives
di: Gangl, Peter, et al.
Pubblicazione: (2024)

Motion Detection in Diffraction Tomography by Common Circle Methods
di: Quellmalz, Michael, et al.
Pubblicazione: (2022)

Inverse Problems of Trapped Objects
di: Elbau, Peter, et al.
Pubblicazione: (2019)

Using the Transport of Intensity and the Transport of Phase Equation for Phase Retrieval
di: Kirisits, Clemens, et al.
Pubblicazione: (2024)

Computational inverse scattering with internal sources: a reproducing kernel Hilbert space approach
di: Dong, Yakun, et al.
Pubblicazione: (2024)

Solving parametric elliptic interface problems via interfaced operator network
di: Wu, Sidi, et al.
Pubblicazione: (2023)

Variably Scaled Kernels for the regularized solution of the parametric Fourier imaging problem
di: Volpara, Anna, et al.
Pubblicazione: (2025)

General transformation neural networks: A class of parametrized functions for high-dimensional function approximation
di: Wang, Xiaoyang, et al.
Pubblicazione: (2025)

Spectral coefficient learning physics informed neural network for time-dependent fractional parametric differential problems
di: Sivalingam, S M, et al.
Pubblicazione: (2025)

On the importance of the $\varepsilon$-regularization of the distribution-dependent Mumford-Shah model for hyperspectral image segmentation
di: Cohrs, Jan-Christopher, et al.
Pubblicazione: (2024)

Neural parametric representations for thin-shell shape optimisation
di: Xiao, Xiao, et al.
Pubblicazione: (2026)

Regularized dynamical parametric approximation
di: Feischl, Michael, et al.
Pubblicazione: (2024)

Convergence rates for Tikhonov regularization on compact sets: application to neural networks
di: Palumbo, Barbara, et al.
Pubblicazione: (2025)

Approximation of Set-Valued Functions with images sets in $\mathbb{R}^d$
di: Dyn, Nira, et al.
Pubblicazione: (2025)

Neural networks for the approximation of Euler's elastica
di: Celledoni, Elena, et al.
Pubblicazione: (2023)

A Mathematical Analysis of Neural Operator Behaviors
di: Le, Vu-Anh, et al.
Pubblicazione: (2024)

Convergence analysis of the dynamically regularized Lagrange multiplier method for the incompressible Navier-Stokes equations
di: Doan, Cao-Kha, et al.
Pubblicazione: (2025)

How Analysis Can Teach Us the Optimal Way to Design Neural Operators
di: Le, Vu-Anh, et al.
Pubblicazione: (2024)

Neural network representation of microflows with BGK model
di: Zhang, Pei, et al.
Pubblicazione: (2024)

Shallow neural network yields regularization for ill-posed inverse problems
di: Wang, Lan, et al.
Pubblicazione: (2025)

Numerical study on hyper parameter settings for neural network approximation to partial differential equations
di: Yang, Hee Jun, et al.
Pubblicazione: (2025)

A tamed-adaptive Milstein scheme for stochastic differential equations with low regularity coefficients
di: Vu, Thi-Huong, et al.
Pubblicazione: (2024)

An extended physics informed neural network for preliminary analysis of parametric optimal control problems
di: Demo, Nicola, et al.
Pubblicazione: (2021)

Concurrent level set and fiber orientation optimization of composite structures
di: Mokhtarzadeh, M., et al.
Pubblicazione: (2024)

Neural network approaches for variance reduction in fluctuation formulas
di: Pavliotis, Grigorios, et al.
Pubblicazione: (2024)

Generative diffusion learning for parametric partial differential equations
di: Wang, Ting, et al.
Pubblicazione: (2023)

Efficient solution of sequences of parametrized Lyapunov equations with applications
di: Palitta, Davide, et al.
Pubblicazione: (2023)

A monotonicity-based globalization of the level-set method for inclusion detection
di: Harrach, Bastian, et al.
Pubblicazione: (2025)

A multilayer level-set method for eikonal-based traveltime tomography
di: Li, Wenbin, et al.
Pubblicazione: (2025)

Two kinds of parametric piecewise rational interpolation kernels for image magnification
di: Guo, Bing, et al.
Pubblicazione: (2025)

On preconditioning and solving an extended class of interval parametric linear systems
di: Skalna, Iwona, et al.
Pubblicazione: (2020)

GLENN: Neural network-enhanced computation of Ginzburg-Landau energy minimizers
di: Crocoll, Michael, et al.
Pubblicazione: (2026)