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 531–540 of 2050 papers

TitleStatusHype
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?Code0
Unsupervised Video Summarization via Iterative Training and Simplified GANCode0
Saturn: Efficient Multi-Large-Model Deep Learning—0
BarcodeBERT: Transformers for Biodiversity AnalysisCode1
Changing the Kernel During Training Leads to Double Descent in Kernel RegressionCode0
An energy-based comparative analysis of common approaches to text classification in the Legal domain—0
Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models—0
Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias—0
NoMoPy: Noise Modeling in Python—0
Optimizing accuracy and diversity: a multi-task approach to forecast combinations—0
Show:102550
← PrevPage 54 of 205Next →

No leaderboard results yet.