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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 81–90 of 2050 papers

TitleStatusHype
Conditional Matrix Flows for Gaussian Graphical ModelsCode1
On Pitfalls of Test-Time AdaptationCode1
Rethinking the Evaluation Protocol of Domain GeneralizationCode1
Automatic Model Selection with Large Language Models for ReasoningCode1
An XAI framework for robust and transparent data-driven wind turbine power curve modelsCode1
You Only Train Once: Learning a General Anomaly Enhancement Network with Random Masks for Hyperspectral Anomaly DetectionCode1
PyVBMC: Efficient Bayesian inference in PythonCode1
Searching for Effective Neural Network Architectures for Heart Murmur Detection from PhonocardiogramCode1
Eryn : A multi-purpose sampler for Bayesian inferenceCode1
Quantifying & Modeling Multimodal Interactions: An Information Decomposition FrameworkCode1
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