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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 601–610 of 2050 papers

TitleStatusHype
Tryage: Real-time, intelligent Routing of User Prompts to Large Language Models—0
Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE DiscoveryCode0
Open, Closed, or Small Language Models for Text Classification?—0
Which Transformer to Favor: A Comparative Analysis of Efficiency in Vision TransformersCode1
No Regularization is Needed: An Efficient and Effective Model for Incomplete Label Distribution Learning—0
LCE: An Augmented Combination of Bagging and Boosting in PythonCode1
Foundation Model is Efficient Multimodal Multitask Model SelectorCode1
A coupled-mechanisms modelling framework for neurodegeneration—0
Updating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance—0
Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration ErrorCode1
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