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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 111–120 of 2050 papers

TitleStatusHype
Entropic Descent Archetypal Analysis for Blind Hyperspectral UnmixingCode1
A new family of Constitutive Artificial Neural Networks towards automated model discoveryCode1
clusterBMA: Bayesian model averaging for clusteringCode1
Efficient End-to-End AutoML via Scalable Search Space DecompositionCode1
HyperImpute: Generalized Iterative Imputation with Automatic Model SelectionCode1
Unsupervised Image Representation Learning with Deep Latent ParticlesCode1
Time Series Anomaly Detection via Reinforcement Learning-Based Model SelectionCode1
NICO++: Towards Better Benchmarking for Domain GeneralizationCode1
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet DatasetsCode1
Monitored Distillation for Positive Congruent Depth CompletionCode1
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