SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 451–460 of 2050 papers

TitleStatusHype
Compressive Recovery of Signals Defined on Perturbed Graphs—0
Understanding Model Selection For Learning In Strategic Environments—0
Unsupervised Optimisation of GNNs for Node Clustering—0
Selective linear segmentation for detecting relevant parameter changes—0
Pretrained Generative Language Models as General Learning Frameworks for Sequence-Based Tasks—0
Tuning In: Analysis of Audio Classifier Performance in Clinical Settings with Limited Data—0
A Bandit Approach with Evolutionary Operators for Model Selection—0
A Bias-Variance Decomposition for Ensembles over Multiple Synthetic DatasetsCode0
Best Practices for Text Annotation with Large Language Models—0
Absolute convergence and error thresholds in non-active adaptive sampling—0
Show:102550
← PrevPage 46 of 205Next →

No leaderboard results yet.