{"total":50,"rows":[{"id":"2306.08321","title":"Nonparametric regression using over-parameterized shallow ReLU neural networks","created":"2023-06-14","cited_by_count":24,"area":null},{"id":"2301.09554","title":"Deep Learning Meets Sparse Regularization: A Signal Processing Perspective","created":"2023-01-23","cited_by_count":21,"area":null},{"id":"2203.02543","title":"Ridges, Neural Networks, and the Radon Transform","created":"2022-03-04","cited_by_count":18,"area":null},{"id":"2305.16534","title":"Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression","created":"2023-05-25","cited_by_count":18,"area":null},{"id":"2211.05020","title":"Duality for Neural Networks through Reproducing Kernel Banach Spaces","created":"2022-11-09","cited_by_count":15,"area":null},{"id":"2304.01561","title":"Optimal rates of approximation by shallow ReLU$^k$ neural networks and applications to nonparametric regression","created":"2023-04-04","cited_by_count":15,"area":null},{"id":"2305.12584","title":"Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces","created":"2023-05-21","cited_by_count":10,"area":null},{"id":"2305.15598","title":"ReLU Neural Networks with Linear Layers are Biased Towards Single- and Multi-Index Models","created":"2023-05-24","cited_by_count":8,"area":null},{"id":"2403.08750","title":"Neural reproducing kernel Banach spaces and representer theorems for deep networks","created":"2024-03-13","cited_by_count":8,"area":null},{"id":"2404.18769","title":"Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks","created":"2024-04-29","cited_by_count":8,"area":null},{"id":"2206.07128","title":"Stability of Image-Reconstruction Algorithms","created":"2022-06-14","cited_by_count":7,"area":null},{"id":"2209.08461","title":"Random Fourier Features for Asymmetric Kernels","created":"2022-09-18","cited_by_count":7,"area":null},{"id":"2406.09860","title":"Dataset Condensation with Latent Quantile Matching","created":"2024-06-14","cited_by_count":6,"area":null},{"id":"2307.01177","title":"Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space","created":"2023-07-03","cited_by_count":5,"area":null},{"id":"2310.03696","title":"Function-Space Optimality of Neural Architectures with Multivariate Nonlinearities","created":"2023-10-05","cited_by_count":5,"area":null},{"id":"2406.02529","title":"ReLUs Are Sufficient for Learning Implicit Neural Representations","created":"2024-06-04","cited_by_count":5,"area":null},{"id":"2411.04108","title":"Weighted Sobolev Approximation Rates for Neural Networks on Unbounded Domains","created":"2024-11-06","cited_by_count":5,"area":null},{"id":"2206.14625","title":"From Kernel Methods to Neural Networks: A Unifying Variational Formulation","created":"2022-06-29","cited_by_count":4,"area":null},{"id":"2305.15839","title":"Embeddings between Barron spaces with higher order activation functions","created":"2023-05-25","cited_by_count":4,"area":null},{"id":"2308.03230","title":"Tractability of approximation by general shallow networks","created":"2023-08-07","cited_by_count":4,"area":null},{"id":"2403.03353","title":"Hypothesis Spaces for Deep Learning","created":"2024-03-05","cited_by_count":4,"area":null},{"id":"2110.03673","title":"Tighter Sparse Approximation Bounds for ReLU Neural Networks","created":"2021-10-07","cited_by_count":3,"area":null},{"id":"2405.10229","title":"Random ReLU Neural Networks as Non-Gaussian Processes","created":"2024-05-16","cited_by_count":3,"area":null},{"id":"2505.07765","title":"Solving Nonlinear PDEs with Sparse Radial Basis Function Networks","created":"2025-05-12","cited_by_count":3,"area":null},{"id":"2209.05544","title":"The Mori-Zwanzig formulation of deep learning","created":"2022-09-12","cited_by_count":2,"area":null},{"id":"2410.02132","title":"Nonuniform random feature models using derivative information","created":"2024-10-03","cited_by_count":2,"area":null},{"id":"2410.14591","title":"A Lipschitz spaces view of infinitely wide shallow neural networks","created":"2024-10-18","cited_by_count":2,"area":null},{"id":"2411.11242","title":"Mirror Descent on Reproducing Kernel Banach Spaces","created":"2024-11-18","cited_by_count":2,"area":null},{"id":"2502.16331","title":"A Gap Between the Gaussian RKHS and Neural Networks: An Infinite-Center Asymptotic Analysis","created":"2025-02-22","cited_by_count":2,"area":null},{"id":"2509.04549","title":"Manipulating Transformer-Based Models: Controllability, Steerability, and Robust Interventions","created":"2025-09-04","cited_by_count":2,"area":null},{"id":"2509.26371","title":"Vector-Valued Reproducing Kernel Banach Spaces for Neural Networks and Operators","created":"2025-09-30","cited_by_count":2,"area":null},{"id":"2302.00205","title":"Gradient Descent in Neural Networks as Sequential Learning in RKBS","created":"2023-02-01","cited_by_count":1,"area":null},{"id":"2305.03890","title":"Approximation by non-symmetric networks for cross-domain learning","created":"2023-05-06","cited_by_count":1,"area":null},{"id":"2311.02838","title":"Barron Space for Graph Convolution Neural Networks","created":"2023-11-06","cited_by_count":1,"area":null},{"id":"2312.02671","title":"Learning a Sparse Representation of Barron Functions with the Inverse Scale Space Flow","created":"2023-12-05","cited_by_count":1,"area":null},{"id":"2510.13868","title":"DeepMartingale: Duality of the Optimal Stopping Problem with Expressivity and High-Dimensional Hedging","created":"2025-10-13","cited_by_count":1,"area":null},{"id":"2601.17562","title":"Sparse RBF Networks for PDEs and nonlocal equations: function space theory, operator calculus, and training algorithms","created":"2026-01-24","cited_by_count":1,"area":null},{"id":"2603.17785","title":"A Dual Certificate Approach to Sparsity in Infinite-Width Shallow Neural Networks","created":"2026-03-18","cited_by_count":1,"area":null},{"id":"2605.08475","title":"Transformers Can Implement Preconditioned Richardson Iteration for In-Context Gaussian Kernel Regression","created":"2026-05-08","cited_by_count":1,"area":null},{"id":"2308.02293","title":"Outlier-robust neural network training: variation regularization meets trimmed loss to prevent functional breakdown","created":"2023-08-04","cited_by_count":0,"area":null},{"id":"2405.15254","title":"Novel Kernel Models and Exact Representor Theory for Neural Networks Beyond the Over-Parameterized Regime","created":"2024-05-24","cited_by_count":0,"area":null},{"id":"2409.18132","title":"Decomposition of one-layer neural networks via the infinite sum of reproducing kernel Banach spaces","created":"2024-08-09","cited_by_count":0,"area":null},{"id":"2410.21696","title":"The Effects of Multi-Task Learning on ReLU Neural Network Functions","created":"2024-10-29","cited_by_count":0,"area":null},{"id":"2504.13543","title":"Irregular Sampling of High-Dimensional Functions in Reproducing Kernel Hilbert Spaces","created":"2025-04-18","cited_by_count":0,"area":null},{"id":"2504.20617","title":"Sobolev norm inconsistency of kernel interpolation","created":"2025-04-29","cited_by_count":0,"area":null},{"id":"2602.22925","title":"Beyond NNGP: Large Deviations and Feature Learning in Bayesian Neural Networks","created":"2026-02-26","cited_by_count":0,"area":null},{"id":"2603.17160","title":"Self-Regularized Learning Methods","created":"2026-03-17","cited_by_count":0,"area":null},{"id":"2605.08176","title":"Physics-Modeled Neural Networks","created":"2026-05-05","cited_by_count":0,"area":null},{"id":"2605.23151","title":"Convex Hybrid Modeling: An Operator-Based Approach","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2605.28106","title":"Gaussian Processes with Sample Paths in Reproducing Kernel Banach Spaces","created":"2026-05-27","cited_by_count":0,"area":null}],"next_cursor":null}