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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 711–720 of 2050 papers

TitleStatusHype
To token or not to token: A Comparative Study of Text Representations for Cross-Lingual TransferCode0
On the Computational Complexity of Private High-dimensional Model SelectionCode0
Transformers for Green Semantic Communication: Less Energy, More SemanticsCode0
Risk Aware Benchmarking of Large Language Models—0
Democratizing LLMs: An Exploration of Cost-Performance Trade-offs in Self-Refined Open-Source Models—0
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
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