Copertina

Sharp Convergence Rates for Matching Pursuit

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Klusowski, Jason M., Siegel, Jonathan W.
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning Numerical Analysis
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces
di: Siegel, Jonathan W.
Pubblicazione: (2022)

Weighted variation spaces and approximation by shallow ReLU networks
di: DeVore, Ronald, et al.
Pubblicazione: (2023)

Quantitative Approximation Rates for Group Equivariant Learning
di: Siegel, Jonathan W., et al.
Pubblicazione: (2026)

Convergence Rates for Learning Pseudo-Differential Operators
di: Chen, Jiaheng, et al.
Pubblicazione: (2026)

Sharp Analysis of Power Iteration for Tensor PCA
di: Wu, Yuchen, et al.
Pubblicazione: (2024)

A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models
di: Li, Gen, et al.
Pubblicazione: (2024)

Sharp Lower Bounds for Linearized ReLU^k Approximation on the Sphere
di: Mao, Tong, et al.
Pubblicazione: (2025)

Optimal Recovery Meets Minimax Estimation
di: DeVore, Ronald, et al.
Pubblicazione: (2025)

Approximation Rates for Shallow ReLU$^k$ Neural Networks on Sobolev Spaces via the Radon Transform
di: Mao, Tong, et al.
Pubblicazione: (2024)

Convergence analysis of OT-Flow for sample generation
di: Jing, Yang, et al.
Pubblicazione: (2024)

Sharp Lower Bounds on the Manifold Widths of Sobolev and Besov Spaces
di: Siegel, Jonathan W.
Pubblicazione: (2024)

Trajectory Generator Matching for Time Series
di: Jahn, T., et al.
Pubblicazione: (2025)

On the Convergence of Irregular Sampling in Reproducing Kernel Hilbert Spaces
di: Iske, Armin
Pubblicazione: (2025)

Mode-wise Principal Subspace Pursuit and Matrix Spiked Covariance Model
di: Tang, Runshi, et al.
Pubblicazione: (2023)

Beyond Derivative Pathology of PINNs: Variable Splitting Strategy with Convergence Analysis
di: Park, Yesom, et al.
Pubblicazione: (2024)

Lower Bounds for the Convergence of Tensor Power Iteration on Random Overcomplete Models
di: Wu, Yuchen, et al.
Pubblicazione: (2022)

Quantifying Training Difficulty and Accelerating Convergence in Neural Network-Based PDE Solvers
di: Chen, Chuqi, et al.
Pubblicazione: (2024)

MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence
di: Modoranu, Ionut-Vlad, et al.
Pubblicazione: (2024)

Computational Advantages of Multi-Grade Deep Learning: Convergence Analysis and Performance Insights
di: Fang, Ronglong, et al.
Pubblicazione: (2025)

Geometric Layer-wise Approximation Rates for Deep Networks
di: Zhang, Shijun, et al.
Pubblicazione: (2026)

Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks
di: Jin, Bangti, et al.
Pubblicazione: (2025)

Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise
di: Hou, Yilong, et al.
Pubblicazione: (2024)

Sketchy Moment Matching: Toward Fast and Provable Data Selection for Finetuning
di: Dong, Yijun, et al.
Pubblicazione: (2024)

Convergence and Sketching-Based Efficient Computation of Neural Tangent Kernel Weights in Physics-Based Loss
di: Hirsch, Max, et al.
Pubblicazione: (2025)

Exact Gaussian Moment Matching for Residual Networks: a Second-Order Method
di: Kuang, Simon, et al.
Pubblicazione: (2026)

Towards Sharp Minimax Risk Bounds for Operator Learning
di: Adcock, Ben, et al.
Pubblicazione: (2025)

Convergence and error control of consistent PINNs for elliptic PDEs
di: Bonito, Andrea, et al.
Pubblicazione: (2024)

Learning Neural PDE Solvers with Convergence Guarantees
di: Hsieh, Jun-Ting, et al.
Pubblicazione: (2019)

Higher Order Approximation Rates for ReLU CNNs in Korobov Spaces
di: Li, Yuwen, et al.
Pubblicazione: (2025)

Optimal Approximation of Zonoids and Uniform Approximation by Shallow Neural Networks
di: Siegel, Jonathan W.
Pubblicazione: (2023)

Approximation with SiLU Networks: Constant Depth and Exponential Rates for Basic Operations
di: Ayena, Koffi O.
Pubblicazione: (2025)

Smoothness Adaptivity in Constant-Depth Neural Networks: Optimal Rates via Smooth Activations
di: Liu, Yuhao, et al.
Pubblicazione: (2026)

Riemannian Langevin Dynamics: Strong Convergence of Geometric Euler-Maruyama Scheme
di: Zhan, Zhiyuan, et al.
Pubblicazione: (2026)

Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models
di: Huang, Daniel Zhengyu, et al.
Pubblicazione: (2025)

Convergence Analysis of Probability Flow ODE for Score-based Generative Models
di: Huang, Daniel Zhengyu, et al.
Pubblicazione: (2024)

GeoFunFlow-3D: A Physics-Guided Generative Flow Matching Framework for High-Fidelity 3D Aerodynamic Inference over Complex Geometries
di: Jiang, Ruiling, et al.
Pubblicazione: (2026)

Discrepancies are Virtue: Weak-to-Strong Generalization through Lens of Intrinsic Dimension
di: Dong, Yijun, et al.
Pubblicazione: (2025)

Convergence rates for gradient descent in the training of overparameterized artificial neural networks with piecewise affine activation
di: Jentzen, Arnulf, et al.
Pubblicazione: (2021)

Quantitative Convergences of Lie Group Momentum Optimizers
di: Kong, Lingkai, et al.
Pubblicazione: (2024)

Convergence analysis of kernel learning FBSDE filter
di: Lyu, Yunzheng, et al.
Pubblicazione: (2024)