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 401–410 of 2050 papers

TitleStatusHype
Iterative Hard Thresholding for Model Selection in Genome-Wide Association StudiesCode0
Joint Inference for Neural Network Depth and Dropout RegularizationCode0
LaF: Labeling-Free Model Selection for Automated Deep Neural Network ReusingCode0
Choosing the Number of Topics in LDA Models -- A Monte Carlo Comparison of Selection CriteriaCode0
An Offline Metric for the Debiasedness of Click ModelsCode0
Large Language Models for Constructing and Optimizing Machine Learning Workflows: A SurveyCode0
Evaluating LLP Methods: Challenges and ApproachesCode0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
Learning Conditional Invariance through Cycle ConsistencyCode0
Evaluation of HTR models without Ground Truth MaterialCode0
Show:102550
← PrevPage 41 of 205Next →

No leaderboard results yet.