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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 191–200 of 2050 papers

TitleStatusHype
Evaluating Weakly Supervised Object Localization Methods RightCode1
Source Model Selection for Deep Learning in the Time Series DomainCode1
InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive RegularizersCode1
One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image SegmentationCode1
Exploiting BERT for End-to-End Aspect-based Sentiment AnalysisCode1
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANsCode1
Interpretable multiclass classification by MDL-based rule listsCode1
BERTScore: Evaluating Text Generation with BERTCode1
Forecasting with time series imagingCode1
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible EvaluationCode1
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