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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 741–750 of 2050 papers

TitleStatusHype
A novel algebraic approach to time-reversible evolutionary modelsCode0
Parameter identifiability and model selection for partial differential equation models of cell invasionCode0
Generalized Information Criteria for Structured Sparse Models—0
AutoML-GPT: Large Language Model for AutoML—0
Subjectivity in Unsupervised Machine Learning Model Selection—0
SortedNet: A Scalable and Generalized Framework for Training Modular Deep Neural Networks—0
Exploring Model Transferability through the Lens of Potential EnergyCode0
Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities—0
Sparse Models for Machine Learning—0
Tryage: Real-time, intelligent Routing of User Prompts to Large Language Models—0
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