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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 1081–1090 of 2050 papers

TitleStatusHype
Fast approximations of the Jeffreys divergence between univariate Gaussian mixture models via exponential polynomial densities—0
Gaussian Process Subspace Regression for Model ReductionCode0
Parsimony-Enhanced Sparse Bayesian Learning for Robust Discovery of Partial Differential EquationsCode0
Federated Model Search via Reinforcement Learning—0
Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss FunctionsCode0
Model Selection for Generic Contextual Bandits—0
Unsupervised Model Drift Estimation with Batch Normalization Statistics for Dataset Shift Detection and Model Selection—0
Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images—0
Laplace Redux -- Effortless Bayesian Deep LearningCode1
Using deep learning to detect patients at risk for prostate cancer despite benign biopsies—0
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