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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

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Showing 361–370 of 2050 papers

TitleStatusHype
Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge DistillationCode0
Approximate Cross-validation: Guarantees for Model Assessment and SelectionCode0
A Deep Neural Network Surrogate Modeling Benchmark for Temperature Field Prediction of Heat Source LayoutCode0
EPP: interpretable score of model predictive powerCode0
A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts modelsCode0
GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnaceCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
A Deep Learning Method for Comparing Bayesian Hierarchical ModelsCode0
GestureGPT: Toward Zero-Shot Free-Form Hand Gesture Understanding with Large Language Model AgentsCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
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