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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 871–880 of 2050 papers

TitleStatusHype
A Deep Learning Method for Comparing Bayesian Hierarchical ModelsCode0
Warlock: an automated computational workflow for simulating spatially structured tumour evolutionCode0
The #DNN-Verification Problem: Counting Unsafe Inputs for Deep Neural Networks—0
Transformers as Algorithms: Generalization and Stability in In-context LearningCode0
Understanding Best Subset Selection: A Tale of Two C(omplex)ities—0
Guided Recommendation for Model Fine-Tuning—0
BiasBed - Rigorous Texture Bias EvaluationCode0
A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countriesCode0
Bayesian Interpolation with Deep Linear Networks—0
Choosing the Number of Topics in LDA Models -- A Monte Carlo Comparison of Selection CriteriaCode0
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