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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 1081–1090 of 2050 papers

TitleStatusHype
Realistic Evaluation of Test-Time Adaptation Algorithms: Unsupervised Hyperparameter Selection—0
Real-time Monocular Depth Estimation with Sparse Supervision on Mobile—0
Reassessing Large Language Model Boolean Query Generation for Systematic Reviews—0
Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood—0
Recommending Pre-Trained Models for IoT Devices—0
Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence—0
Reduced-order modeling using Dynamic Mode Decomposition and Least Angle Regression—0
ReeM: Ensemble Building Thermodynamics Model for Efficient HVAC Control via Hierarchical Reinforcement Learning—0
Reframing Neural Networks: Deep Structure in Overcomplete Representations—0
Region Detection in Markov Random Fields: Gaussian Case—0
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