Copertina

Fixed-Point Neural Optimal Transport without Implicit Differentiation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Park, Yesom, Gelphman, Eric, Osher, Stanley, Fung, Samy Wu
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Optimization and Control Machine Learning 49Q22, 68T07, 65K10
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

On the Convergence of Jacobian-Free Backpropagation for Optimal Control Problems with Implicit Hamiltonians
di: Gelphman, Eric, et al.
Pubblicazione: (2026)

Meshless Shape Optimization using Neural Networks and Partial Differential Equations on Graphs
di: Martinet, Eloi, et al.
Pubblicazione: (2025)

The Pontryagin Maximum Principle for Training Convolutional Neural Networks
di: Hofmann, Sebastian, et al.
Pubblicazione: (2025)

A Mollification Approach to Ramified Transport and Tree Shape Optimization
di: Bressan, Alberto, et al.
Pubblicazione: (2026)

No-Regret Generative Modeling via Parabolic Monge-Ampère PDE
di: Deb, Nabarun, et al.
Pubblicazione: (2025)

Variational conditional normalizing flows for computing second-order mean field control problems
di: Zhao, Jiaxi, et al.
Pubblicazione: (2025)

A First Step Towards Mesh-Free Probabilistic Shape Optimization
di: Schmidt, Stephan, et al.
Pubblicazione: (2026)

An Operator Learning Approach to Nonsmooth Optimal Control of Nonlinear PDEs
di: Song, Yongcun, et al.
Pubblicazione: (2024)

SympFormer: Accelerated attention blocks via Inertial Dynamics on Density Manifolds
di: Stein, Viktor, et al.
Pubblicazione: (2026)

A supervised learning scheme for computing Hamilton-Jacobi equation via density coupling
di: Cui, Jianbo, et al.
Pubblicazione: (2024)

Convergence, design and training of continuous-time dropout as a random batch method
di: Álvarez-López, Antonio, et al.
Pubblicazione: (2025)

Log-Averaged Mirror Prox for Fast, Large-Scale Optimal Transport in Linear Space
di: Burns, Matthew X., et al.
Pubblicazione: (2025)

An Efficient Augmented Lagrangian Method for Dynamic Optimal Transport on Surfaces Based on Second-Order Cone Programming
di: Chen, Liang, et al.
Pubblicazione: (2025)

Differential-Equation Constrained Optimization With Stochasticity
di: Li, Qin, et al.
Pubblicazione: (2023)

Computing Optimal Trajectories for Optimal Transport in Nonuniform Environments
di: Dieci, Luca, et al.
Pubblicazione: (2025)

Implicit Regularization of Large Neural Networks via Mean-Field Formulation
di: Acciaio, Beatrice, et al.
Pubblicazione: (2026)

Stochastic homogenisation of nonlinear minimum-cost flow problems
di: Gladbach, Peter, et al.
Pubblicazione: (2024)

A unified approach to shape and topological sensitivity analysis of discretized optimal design problems
di: Gangl, Peter, et al.
Pubblicazione: (2022)

Stochastic Mirror Descent for Convex Optimization with Consensus Constraints
di: Borovykh, Anastasia, et al.
Pubblicazione: (2022)

Approximation of Splines in Wasserstein Spaces
di: Justiniano, Jorge, et al.
Pubblicazione: (2023)

Entropy-Regularized Optimal Transport in Information Design
di: Justiniano, Jorge, et al.
Pubblicazione: (2024)

Retraction based regression methods on Riemannian manifolds
di: Loayza-Romero, Estefanía, et al.
Pubblicazione: (2026)

On the importance of smoothness, interface resolution and numerical sensitivities in shape and topological sensitivity analysis
di: Gfrerer, M. H., et al.
Pubblicazione: (2026)

Quantitative convergence of a discretization of dynamic optimal transport using the dual formulation
di: Ishida, Sadashige, et al.
Pubblicazione: (2023)

Cavity shape reconstruction with a homogeneous Robin condition via a constrained coupled complex boundary method with ADMM
di: Essahraoui, Mustapha, et al.
Pubblicazione: (2026)

Enhanced shape recovery in advection--diffusion problems via a novel ADMM-based CCBM optimization
di: Cherrat, Elmehdi, et al.
Pubblicazione: (2025)

Discrete-to-continuum limits of optimal transport with linear growth on periodic graphs
di: Portinale, Lorenzo, et al.
Pubblicazione: (2023)

Quantitative Convergence of Wasserstein Gradient Flows of Kernel Mean Discrepancies
di: Chizat, Lénaïc, et al.
Pubblicazione: (2026)

Inverse Problems Over Probability Measure Space
di: Li, Qin, et al.
Pubblicazione: (2025)

Expansive Natural Neural Gradient Flows for Energy Minimization
di: Dahmen, Wolfgang, et al.
Pubblicazione: (2025)

Gradient flow for finding E-optimal designs
di: Shi, Jieling, et al.
Pubblicazione: (2026)

Enforcing Mesh Quality Constraints in Shape Optimization with a Gradient Projection Method
di: Blauth, Sebastian, et al.
Pubblicazione: (2024)

Recent Advances in Non-convex Smoothness Conditions and Applicability to Deep Linear Neural Networks
di: Patel, Vivak, et al.
Pubblicazione: (2024)

Oracle-Net for nonlinear compressed sensing in Electrical Impedance Tomography reconstruction problems
di: Lazzaro, Damiana, et al.
Pubblicazione: (2024)

Explainable post-training bias mitigation with distribution-based fairness metrics
di: Franks, Ryan, et al.
Pubblicazione: (2025)

Detecting immersed obstacle in Stokes fluid flow using the coupled complex boundary method
di: Rabago, Julius Fergy Tiongson, et al.
Pubblicazione: (2024)

Shape Gradient Based Non-Parametric Mumford-Shah Segmentation Without Level Sets
di: P, Shafeequdheen, et al.
Pubblicazione: (2025)

Generalized sparsity-promoting solvers for Bayesian inverse problems: Versatile sparsifying transforms and unknown noise variances
di: Lindbloom, Jonathan, et al.
Pubblicazione: (2024)

A Symplectic Discretization Based Proximal Point Algorithm for Convex Minimization
di: Yuan, Ya-xiang, et al.
Pubblicazione: (2024)

A nonsmooth primal-dual method with interwoven PDE constraint solver
di: Jensen, Bjørn, et al.
Pubblicazione: (2022)