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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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4 results for “NNK”

Neighborhood and Graph Constructions using Non-Negative Kernel Regressioncs.LG

NNK algorithm improves neighborhood and graph construction for machine learning.

problem Ad hoc selection of k and ε parameters in kNN and ε-neighborhood methods.

method NNK algorithm for better sparse signal approximation.

result NNK leads to superior performance in local neighborhood and graph-based machine learning tasks.

NNK neighborhood construction sparse approximation machine learning signal processing

2019-10-21abs ↗pdf ↗

Channel-Wise Early Stopping without a Validation Set via NNK Polytope Interpolationcs.LG

A new method for early stopping in neural networks without validation sets.

problem Determining when to stop training neural networks to avoid overfitting.

method Channel-wise DeepNNK (CW-DeepNNK) using non-negative kernel regression and polytope interpolation.

result The proposed early stopping criterion based on CW-DeepNNK performs better than standard validation-based methods.

neural networks early stopping non-negative kernel regression polytope interpolation channel-wise

2021-07-27abs ↗pdf ↗

Generalization Ability of Wide Neural Networks on

\mathbb{R}

stat.ML

Wide neural networks on R generalize well with early stopping.

problem Understanding generalization in wide neural networks.

method Analysis of spectral properties of NTK and NNK, convergence of NNK to NTK, minimax rates, and early stopping strategy.

result Wide neural networks trained with early stopping achieve the minimax rate and generalize well.

wide neural networks generalization spectral properties early stopping minimax rate

2023-02-12abs ↗pdf ↗

DeepNNK: Explaining deep models and their generalization using polytope interpolationcs.LG

Paper introduces a method to explain deep learning models and identify good generalization.

problem Limited interpretability of neural networks hinders progress and real-world applications.

method Polytope interpolation method for local explainability and generalization assessment.

result Developed a method to identify deep learning models with good generalization properties.

Neural Networks Interpretability Generalization Polytope Interpolation Leave-One-Out Estimation

2020-07-20abs ↗pdf ↗