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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 231–240 of 2050 papers

TitleStatusHype
Energy-Aware Dynamic Neural Inference—0
DSDE: Using Proportion Estimation to Improve Model Selection for Out-of-Distribution Detection—0
Dirichlet process mixtures of block g priors for model selection and prediction in linear modelsCode0
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference—0
Efficient Model Compression for Bayesian Neural Networks—0
MESS+: Energy-Optimal Inferencing in Language Model Zoos with Service Level Guarantees—0
Leveraging LLMs for MT in Crisis Scenarios: a blueprint for low-resource languages—0
Power side-channel leakage localization through adversarial training of deep neural networksCode0
Model-free Estimation of Latent Structure via Multiscale Nonparametric Maximum Likelihood—0
Bayesian Regression for Predicting Subscription to Bank Term Deposits in Direct Marketing Campaigns—0
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