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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 651–660 of 2050 papers

TitleStatusHype
Frame Fusion with Vehicle Motion Prediction for 3D Object Detection—0
Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization—0
AQuA: A Benchmarking Tool for Label Quality AssessmentCode1
LOVM: Language-Only Vision Model SelectionCode1
Conditional Matrix Flows for Gaussian Graphical ModelsCode1
Sliding Window Neural Generated Tracking Based on Measurement Model—0
Two-level histograms for dealing with outliers and heavy tail distributions—0
Gibbs-Based Information Criteria and the Over-Parameterized Regime—0
Stochastic Marginal Likelihood Gradients using Neural Tangent KernelsCode0
On Pitfalls of Test-Time AdaptationCode1
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