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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 1331–1340 of 2050 papers

TitleStatusHype
Knowledge Guided Encoder-Decoder Framework: Integrating Multiple Physical Models for Agricultural Ecosystem Modeling—0
Label-Efficient Model Selection for Text Generation—0
Label-Only Membership Inference Attack against Node-Level Graph Neural Networks—0
LalaEval: A Holistic Human Evaluation Framework for Domain-Specific Large Language Models—0
Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT—0
Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports—0
Laplace Redux - Effortless Bayesian Deep Learning—0
Laplace's Method Approximations for Probabilistic Inference in Belief Networks with Continuous Variables—0
Large Language Models for History, Philosophy, and Sociology of Science: Interpretive Uses, Methodological Challenges, and Critical Perspectives—0
Large-scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer's Disease Across Multiple Institutions—0
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