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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 981–990 of 2050 papers

TitleStatusHype
Diagnostic Tool for Out-of-Sample Model EvaluationCode0
Hyperparameter Importance of Quantum Neural Networks Across Small Datasets—0
MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-LearningCode0
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning—0
Robust Information Criterion for Model Selection in Sparse High-Dimensional Linear Regression Models—0
Learning diffusion coefficients, kinetic parameters, and the number of underlying states from a multi-state diffusion process: robustness results and application to PDK1/PKCα, dynamicsCode0
Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble SolutionCode0
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation—0
Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge DistillationCode0
Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound—0
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