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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 581–590 of 2050 papers

TitleStatusHype
GLEMOS: Benchmark for Instantaneous Graph Learning Model SelectionCode0
Learning the mechanisms of network growthCode0
Beyond One-Size-Fits-All: Multi-Domain, Multi-Task Framework for Embedding Model Selection—0
Bayesian Nonparametrics: An Alternative to Deep Learning—0
Individual Text Corpora Predict Openness, Interests, Knowledge and Level of Education—0
EL-MLFFs: Ensemble Learning of Machine Leaning Force Fields—0
Carbon Intensity-Aware Adaptive Inference of DNNs—0
Conformal online model aggregationCode0
An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced ClassificationCode0
Bridge the Modality and Capability Gaps in Vision-Language Model Selection—0
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