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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 1726–1750 of 2050 papers

TitleStatusHype
DSV: An Alignment Validation Loss for Self-supervised Outlier Model SelectionCode0
Driver Identification by an Ensemble of CNNs Obtained from Majority-Voting Model SelectionCode0
Model Selection for Bayesian AutoencodersCode0
To token or not to token: A Comparative Study of Text Representations for Cross-Lingual TransferCode0
Free Lunch: Robust Cross-Lingual Transfer via Model Checkpoint AveragingCode0
Automated Model Selection for Tabular DataCode0
Model Selection for Cross-Lingual TransferCode0
Scikit-learn: Machine Learning in PythonCode0
Tune: A Research Platform for Distributed Model Selection and TrainingCode0
Phase Transitions and a Model Order Selection Criterion for Spectral Graph ClusteringCode0
Don't Waste Your Time: Early Stopping Cross-ValidationCode0
Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity 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
MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-LearningCode0
What the HellaSwag? On the Validity of Common-Sense Reasoning BenchmarksCode0
Bolasso: model consistent Lasso estimation through the bootstrapCode0
Towards Accurate Model Selection in Deep Unsupervised Domain AdaptationCode0
Gaussian Process Subspace Regression for Model ReductionCode0
GeMID: Generalizable Models for IoT Device IdentificationCode0
Big model only for hard audios: Sample dependent Whisper model selection for efficient inferencesCode0
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological InferenceCode0
Generalised Linear Models for Dependent Binary Outcomes with Applications to Household Stratified Pandemic Influenza DataCode0
Por Qué Não Utiliser Alla Språk? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual TransferCode0
SecretGen: Privacy Recovery on Pre-Trained Models via Distribution DiscriminationCode0
Symmetric Linear Bandits with Hidden SymmetryCode0
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