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 671–680 of 2050 papers

TitleStatusHype
Cox process representation and inference for stochastic reaction-diffusion processes—0
Coupled differentiation and division of embryonic stem cells inferred from clonal snapshots—0
Cost-Sensitive Learning for Predictive Maintenance—0
Automated discovery of interpretable hyperelastic material models for human brain tissue with EUCLID—0
A Multi-objective Exploratory Procedure for Regression Model Selection—0
Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach—0
Cost-efficient Knowledge-based Question Answering with Large Language Models—0
Cost-Effective Online Contextual Model Selection—0
Correcting Model Bias with Sparse Implicit Processes—0
AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting—0
Show:102550
← PrevPage 68 of 205Next →

No leaderboard results yet.