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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 611–620 of 2050 papers

TitleStatusHype
Entity Set Search of Scientific Literature: An Unsupervised Ranking ApproachCode0
Embarrassingly Simple Performance Prediction for Abductive Natural Language InferenceCode0
Iterative Hard Thresholding for Model Selection in Genome-Wide Association StudiesCode0
Joint Inference for Neural Network Depth and Dropout RegularizationCode0
Don't Waste Your Time: Early Stopping Cross-ValidationCode0
A-DARTS: Stable Model Selection for Data Repair in Time SeriesCode0
Dynamic Interpretability for Model Comparison via Decision RulesCode0
Large Language Models for Constructing and Optimizing Machine Learning Workflows: A SurveyCode0
Effective Stabilized Self-Training on Few-Labeled Graph DataCode0
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time seriesCode0
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