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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 191–200 of 2050 papers

TitleStatusHype
Know2Vec: A Black-Box Proxy for Neural Network RetrievalCode0
YOLOv11 Optimization for Efficient Resource UtilizationCode0
From Human Annotation to LLMs: SILICON Annotation Workflow for Management Research—0
Quantum Machine Learning in Log-based Anomaly Detection: Challenges and Opportunities—0
AD-LLM: Benchmarking Large Language Models for Anomaly DetectionCode1
Investigating the Impact of Balancing, Filtering, and Complexity on Predictive Multiplicity: A Data-Centric PerspectiveCode0
Deep Clustering using Dirichlet Process Gaussian Mixture and Alpha Jensen-Shannon Divergence Clustering Loss—0
Foundational Large Language Models for Materials ResearchCode2
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection—0
NLP-ADBench: NLP Anomaly Detection BenchmarkCode1
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