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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 1031–1040 of 2050 papers

TitleStatusHype
Predictive Coarse-Graining—0
Predictive Matrix-Variate t Models—0
Predictive Modeling through Hyper-Bayesian Optimization—0
Predictive Model Selection for Transfer Learning in Sequence Labeling Tasks—0
Predictive Quantile Regression with Mixed Roots and Increasing Dimensions: The ALQR Approach—0
Predictive variational autoencoder for learning robust representations of time-series data—0
Pretrained Generative Language Models as General Learning Frameworks for Sequence-Based Tasks—0
Pre-Trained Model Recommendation for Downstream Fine-tuning—0
Vision-Language Model Selection and Reuse for Downstream Adaptation—0
Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models—0
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