Parametrizing Convex Sets Using Sublinear Neural Networks
Fuente:
arXiv
Guardado en:
| Autor principal: | Martinet, Eloi |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Minimisation of Quasar-Convex Functions Using Random Zeroth-Order Oracles
por: Farzin, Amir Ali, et al.
Publicado: (2025)
por: Farzin, Amir Ali, et al.
Publicado: (2025)
Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks
por: Jnini, Anas, et al.
Publicado: (2026)
por: Jnini, Anas, et al.
Publicado: (2026)
PRISM: Distribution-free Adaptive Computation of Matrix Functions for Accelerating Neural Network Training
por: Yang, Shenghao, et al.
Publicado: (2026)
por: Yang, Shenghao, et al.
Publicado: (2026)
Min-Max Optimisation for Nonconvex-Nonconcave Functions Using a Random Zeroth-Order Extragradient Algorithm
por: Farzin, Amir Ali, et al.
Publicado: (2025)
por: Farzin, Amir Ali, et al.
Publicado: (2025)
Meshless Shape Optimization using Neural Networks and Partial Differential Equations on Graphs
por: Martinet, Eloi, et al.
Publicado: (2025)
por: Martinet, Eloi, et al.
Publicado: (2025)
Transformers Can Implement Preconditioned Richardson Iteration for In-Context Gaussian Kernel Regression
por: Yan, Mingsong, et al.
Publicado: (2026)
por: Yan, Mingsong, et al.
Publicado: (2026)
Muon is Not That Special: Random or Inverted Spectra Work Just as Well
por: Shumaylov, Zakhar, et al.
Publicado: (2026)
por: Shumaylov, Zakhar, et al.
Publicado: (2026)
A second-order method landing on the Stiefel manifold via Newton$\unicode{x2013}$Schulz iteration
por: Xiong, Xinhui, et al.
Publicado: (2026)
por: Xiong, Xinhui, et al.
Publicado: (2026)
Geometric Data Valuation via Leverage Scores
por: Mendoza-Smith, Rodrigo
Publicado: (2025)
por: Mendoza-Smith, Rodrigo
Publicado: (2025)
Scaling physics-informed hard constraints with mixture-of-experts
por: Chalapathi, Nithin, et al.
Publicado: (2024)
por: Chalapathi, Nithin, et al.
Publicado: (2024)
Learning Explicitly Conditioned Sparsifying Transforms
por: Pătraşcu, Andrei, et al.
Publicado: (2024)
por: Pătraşcu, Andrei, et al.
Publicado: (2024)
A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization
por: Lu, Songtao
Publicado: (2022)
por: Lu, Songtao
Publicado: (2022)
Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks
por: Dereventsov, Anton, et al.
Publicado: (2022)
por: Dereventsov, Anton, et al.
Publicado: (2022)
Maximum Principle of Optimal Probability Density Control
por: Gaby, Nathan, et al.
Publicado: (2025)
por: Gaby, Nathan, et al.
Publicado: (2025)
Universal Approximation of Nonlinear Operators and Their Derivatives
por: de Feo, Filippo
Publicado: (2026)
por: de Feo, Filippo
Publicado: (2026)
Adaptive Proximal Gradient Method for Convex Optimization
por: Malitsky, Yura, et al.
Publicado: (2023)
por: Malitsky, Yura, et al.
Publicado: (2023)
UAdam: Unified Adam-Type Algorithmic Framework for Non-Convex Stochastic Optimization
por: Jiang, Yiming, et al.
Publicado: (2023)
por: Jiang, Yiming, et al.
Publicado: (2023)
Solving Elliptic Optimal Control Problems via Neural Networks and Optimality System
por: Dai, Yongcheng, et al.
Publicado: (2023)
por: Dai, Yongcheng, et al.
Publicado: (2023)
AutoBalance: An Automatic Balancing Framework for Training Physics-Informed Neural Networks
por: An, Kang, et al.
Publicado: (2025)
por: An, Kang, et al.
Publicado: (2025)
PowerStep: Memory-Efficient Adaptive Optimization via $\ell_p$-Norm Steepest Descent
por: Lu, Yao, et al.
Publicado: (2026)
por: Lu, Yao, et al.
Publicado: (2026)
A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equations
por: Liu, Shu, et al.
Publicado: (2024)
por: Liu, Shu, et al.
Publicado: (2024)
Generative Neural Operators of Log-Complexity Can Simultaneously Solve Infinitely Many Convex Programs
por: Kratsios, Anastasis, et al.
Publicado: (2025)
por: Kratsios, Anastasis, et al.
Publicado: (2025)
Dual Cone Gradient Descent for Training Physics-Informed Neural Networks
por: Hwang, Youngsik, et al.
Publicado: (2024)
por: Hwang, Youngsik, et al.
Publicado: (2024)
Multi-level Optimal Control with Neural Surrogate Models
por: Kalise, Dante, et al.
Publicado: (2024)
por: Kalise, Dante, et al.
Publicado: (2024)
Control, Optimal Transport and Neural Differential Equations in Supervised Learning
por: Phung, Minh-Nhat, et al.
Publicado: (2025)
por: Phung, Minh-Nhat, et al.
Publicado: (2025)
Neural incomplete factorization: learning preconditioners for the conjugate gradient method
por: Häusner, Paul, et al.
Publicado: (2023)
por: Häusner, Paul, et al.
Publicado: (2023)
Shape Derivative-Informed Neural Operators with Application to Risk-Averse Shape Optimization
por: Gong, Xindi, et al.
Publicado: (2026)
por: Gong, Xindi, et al.
Publicado: (2026)
ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning
por: Schwencke, Nilo, et al.
Publicado: (2024)
por: Schwencke, Nilo, et al.
Publicado: (2024)
Efficient Trajectory Inference in Wasserstein Space Using Consecutive Averaging
por: Banerjee, Amartya, et al.
Publicado: (2024)
por: Banerjee, Amartya, et al.
Publicado: (2024)
Minimisation of Submodular Functions Using Gaussian Zeroth-Order Random Oracles
por: Farzin, Amir Ali, et al.
Publicado: (2025)
por: Farzin, Amir Ali, et al.
Publicado: (2025)
Using Linearized Optimal Transport to Predict the Evolution of Stochastic Particle Systems
por: Karris, Nicholas, et al.
Publicado: (2024)
por: Karris, Nicholas, et al.
Publicado: (2024)
A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial Networks
por: Mishra, Neel, et al.
Publicado: (2024)
por: Mishra, Neel, et al.
Publicado: (2024)
A Provably-Correct and Robust Convex Model for Smooth Separable NMF
por: Pan, Junjun, et al.
Publicado: (2025)
por: Pan, Junjun, et al.
Publicado: (2025)
KANtrol: A Physics-Informed Kolmogorov-Arnold Network Framework for Solving Multi-Dimensional and Fractional Optimal Control Problems
por: Aghaei, Alireza Afzal
Publicado: (2024)
por: Aghaei, Alireza Afzal
Publicado: (2024)
On the Width Scaling of Neural Optimizers Under Matrix Operator Norms I: Row/Column Normalization and Hyperparameter Transfer
por: Xu, Ruihan, et al.
Publicado: (2026)
por: Xu, Ruihan, et al.
Publicado: (2026)
Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs
por: Heiss, Jakob, et al.
Publicado: (2024)
por: Heiss, Jakob, et al.
Publicado: (2024)
Bayesian Optimization on Networks
por: Li, Wenwen, et al.
Publicado: (2025)
por: Li, Wenwen, et al.
Publicado: (2025)
Introduction to optimization methods for training SciML models
por: Kopaničáková, Alena, et al.
Publicado: (2026)
por: Kopaničáková, Alena, et al.
Publicado: (2026)
Properties of Fixed Points of Generalised Extra Gradient Methods Applied to Min-Max Problems
por: Farzin, Amir Ali, et al.
Publicado: (2025)
por: Farzin, Amir Ali, et al.
Publicado: (2025)
Practical Topics in Optimization
por: Lu, Jun
Publicado: (2025)
por: Lu, Jun
Publicado: (2025)
Ejemplares similares
-
Minimisation of Quasar-Convex Functions Using Random Zeroth-Order Oracles
por: Farzin, Amir Ali, et al.
Publicado: (2025) -
Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks
por: Jnini, Anas, et al.
Publicado: (2026) -
PRISM: Distribution-free Adaptive Computation of Matrix Functions for Accelerating Neural Network Training
por: Yang, Shenghao, et al.
Publicado: (2026) -
Min-Max Optimisation for Nonconvex-Nonconcave Functions Using a Random Zeroth-Order Extragradient Algorithm
por: Farzin, Amir Ali, et al.
Publicado: (2025) -
Meshless Shape Optimization using Neural Networks and Partial Differential Equations on Graphs
por: Martinet, Eloi, et al.
Publicado: (2025)