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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 1051–1060 of 2050 papers

TitleStatusHype
Trinary Tools for Continuously Valued Binary Classifiers—0
TRScore: A Novel GPT-based Readability Scorer for ASR Segmentation and Punctuation model evaluation and selection—0
Trust-Based Cloud Machine Learning Model Selection For Industrial IoT and Smart City Services—0
Truth or Twist? Optimal Model Selection for Reliable Label Flipping Evaluation in LLM-based Counterfactuals—0
Tryage: Real-time, intelligent Routing of User Prompts to Large Language Models—0
Tuning for Trustworthiness -- Balancing Performance and Explanation Consistency in Neural Network Optimization—0
Tuning In: Analysis of Audio Classifier Performance in Clinical Settings with Limited Data—0
Tuning Large language model for End-to-end Speech Translation—0
Tutorial: Modern Theoretical Tools for Understanding and Designing Next-generation Information Retrieval System—0
TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition—0
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