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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 561–570 of 2050 papers

TitleStatusHype
Democratizing LLMs: An Exploration of Cost-Performance Trade-offs in Self-Refined Open-Source Models—0
Rethinking Model Selection and Decoding for Keyphrase Generation with Pre-trained Sequence-to-Sequence ModelsCode1
Exploring the Maze of Multilingual Modeling—0
SC-Safety: A Multi-round Open-ended Question Adversarial Safety Benchmark for Large Language Models in Chinese—0
A Symmetry-based Framework for Model Selection of Coral Reef Population Growth Models—0
Combining UPerNet and ConvNeXt for Contrails Identification to reduce Global WarmingCode0
Test-Time Adaptation Induces Stronger Accuracy and Agreement-on-the-Line—0
Understanding prompt engineering may not require rethinking generalization—0
Driver Identification by an Ensemble of CNNs Obtained from Majority-Voting Model SelectionCode0
A ModelOps-based Framework for Intelligent Medical Knowledge Extraction—0
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