{"total":22,"rows":[{"id":"2405.14630","title":"Bounds for the smallest eigenvalue of the NTK for arbitrary spherical data of arbitrary dimension","created":"2024-05-23","cited_by_count":13,"area":null},{"id":"2307.14531","title":"Controlling the Inductive Bias of Wide Neural Networks by Modifying the Kernel's Spectrum","created":"2023-07-26","cited_by_count":9,"area":null},{"id":"2403.07264","title":"Near-Interpolators: Rapid Norm Growth and the Trade-Off between Interpolation and Generalization","created":"2024-03-12","cited_by_count":8,"area":null},{"id":"2403.12486","title":"NTK-Guided Few-Shot Class Incremental Learning","created":"2024-03-19","cited_by_count":7,"area":null},{"id":"2305.08813","title":"Better NTK Conditioning: A Free Lunch from (ReLU) Nonlinear Activation in Wide Neural Networks","created":"2023-05-15","cited_by_count":6,"area":null},{"id":"2409.06142","title":"Variational Search Distributions","created":"2024-09-10","cited_by_count":6,"area":null},{"id":"2602.01279","title":"Richer Bayesian Last Layers with Subsampled NTK Features","created":"2026-02-01","cited_by_count":4,"area":null},{"id":"2407.17120","title":"Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective","created":"2024-07-24","cited_by_count":3,"area":null},{"id":"2506.22429","title":"Beyond ReLU: How Activations Affect Neural Kernels and Random Wide Networks","created":"2025-06-27","cited_by_count":3,"area":null},{"id":"2511.10796","title":"Fast Generalized Neural Tangent Kernel Statistics via Trace Estimation","created":"2025-11-13","cited_by_count":3,"area":null},{"id":"2508.10490","title":"On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations","created":"2025-08-14","cited_by_count":2,"area":null},{"id":"2312.13259","title":"A note on regularised NTK dynamics with an application to PAC-Bayesian training","created":"2023-12-20","cited_by_count":1,"area":null},{"id":"2601.03162","title":"On the Convergence Behavior of Preconditioned Gradient Descent Toward the Rich Learning Regime","created":"2026-01-06","cited_by_count":1,"area":null},{"id":"2603.29824","title":"Curvature-Guided LoRA: Matching Full Fine-Tuning in Function Space","created":"2026-03-31","cited_by_count":1,"area":null},{"id":"2511.07272","title":"Revisiting the Neural Tangent Kernel: the role of large width and depth","created":"2025-11-10","cited_by_count":0,"area":null},{"id":"2601.06441","title":"FlexAct: Why Learn when you can Pick?","created":"2026-01-10","cited_by_count":0,"area":null},{"id":"2602.14663","title":"Pseudo-differential-enhanced physics-informed neural networks","created":"2026-02-16","cited_by_count":0,"area":null},{"id":"2605.20749","title":"The Devil is in the Condition Numbers: Why is GLU Better than non-GLU Structure?","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2605.25275","title":"Label-NTK Alignments and A Tighter Convergence Bound in the NTK Regime","created":"2026-05-24","cited_by_count":0,"area":null},{"id":"2608.24210","title":"A Data-dependent Early Stopping Rule using Rademacher Complexity with L1-norm","created":"2026-08-25","cited_by_count":0,"area":null},{"id":"2609.39855","title":"Dimension-Free Rank Lifting from Random Hyperplane Arrangements","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.10218","title":"Finite-Sample Approximation of Hessian-Guided Perturbed Wasserstein Gradient Flows","created":"2026-10-07","cited_by_count":0,"area":null}],"next_cursor":null}