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 331–340 of 2050 papers

TitleStatusHype
Modeling flexible behavior with remapping-based hippocampal sequence learning—0
Improving Bias Correction Standards by Quantifying its Effects on Treatment Outcomes—0
Is F_1 Score Suboptimal for Cybersecurity Models? Introducing C_score, a Cost-Aware Alternative for Model Assessment—0
Realistic Evaluation of Test-Time Adaptation Algorithms: Unsupervised Hyperparameter Selection—0
Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity ModelsCode0
A Comprehensive Sustainable Framework for Machine Learning and Artificial Intelligence—0
GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity—0
Subject-driven Text-to-Image Generation via Preference-based Reinforcement LearningCode0
SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse ModalitiesCode1
CLAMS: A System for Zero-Shot Model Selection for Clustering—0
Show:102550
← PrevPage 34 of 205Next →

No leaderboard results yet.