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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 771–780 of 2050 papers

TitleStatusHype
Designing Ecosystems of Intelligence from First Principles—0
Hierarchical Model Selection for Graph Neural Netoworks—0
Rethinking Out-of-Distribution Detection From a Human-Centric Perspective—0
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting—0
Direct-Effect Risk Minimization for Domain GeneralizationCode0
The smooth output assumption, and why deep networks are better than wide ones—0
A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance—0
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing—0
BiasBed -- Rigorous Texture Bias EvaluationCode0
Predicting Biomedical Interactions with Probabilistic Model Selection for Graph Neural Networks—0
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