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 1791–1800 of 2050 papers

TitleStatusHype
Guiding Vision-Language Model Selection for Visual Question-Answering Across Tasks, Domains, and Knowledge TypesCode0
Morphological Segmentation for SenecaCode0
UdL at SemEval-2017 Task 1: Semantic Textual Similarity Estimation of English Sentence Pairs Using Regression Model over Pairwise FeaturesCode0
Comprehensive Evaluation of Deep Learning Architectures for Prediction of DNA/RNA Sequence Binding SpecificitiesCode0
Comparison of Anomaly Detectors: Context MattersCode0
Have I done enough planning or should I plan more?Code0
Comparative Study of Inference Methods for Bayesian Nonnegative Matrix FactorisationCode0
Separating common (global and local) and distinct variation in multiple mixed types data setsCode0
HBIC: A Biclustering Algorithm for Heterogeneous DatasetsCode0
A Test of Relative Similarity For Model Selection in Generative ModelsCode0
Show:102550
← PrevPage 180 of 205Next →

No leaderboard results yet.