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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 311–320 of 2050 papers

TitleStatusHype
Comparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing DataCode0
KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly DetectionCode0
End-to-End Edge AI Service Provisioning Framework in 6G ORAN—0
Unreflected Use of Tabular Data Repositories Can Undermine Research Quality—0
ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks—0
Conformal Prediction with Upper and Lower Bound Models—0
Network Traffic Classification Using Machine Learning, Transformer, and Large Language Models—0
Evaluating Stenosis Detection with Grounding DINO, YOLO, and DINO-DETR—0
BadJudge: Backdoor Vulnerabilities of LLM-as-a-Judge—0
Ranking pre-trained segmentation models for zero-shot transferability—0
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