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 1421–1430 of 2050 papers

TitleStatusHype
MEESO: A Multi-objective End-to-End Self-Optimized Approach for Automatically Building Deep Learning Models—0
MESS+: Energy-Optimal Inferencing in Language Model Zoos with Service Level Guarantees—0
ME-Switch: A Memory-Efficient Expert Switching Framework for Large Language Models—0
Meta-Evaluating Local LLMs: Rethinking Performance Metrics for Serious Games—0
Meta Learning for High-dimensional Ising Model Selection Using _1-regularized Logistic Regression—0
Meta learning of bounds on the Bayes classifier error—0
Meta-Learning PAC-Bayes Priors in Model Averaging—0
MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection—0
Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models—0
Minimally-Supervised Morphological Segmentation using Adaptor Grammars—0
Show:102550
← PrevPage 143 of 205Next →

No leaderboard results yet.