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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 331–340 of 2050 papers

TitleStatusHype
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
Evaluation of HTR models without Ground Truth MaterialCode0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
Beyond One-Size-Fits-All: Tailored Benchmarks for Efficient EvaluationCode0
EPP: interpretable score of model predictive powerCode0
A novel algebraic approach to time-reversible evolutionary modelsCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a ReviewCode0
Evaluating LLP Methods: Challenges and ApproachesCode0
A Normative Theory for Causal Inference and Bayes Factor Computation in Neural CircuitsCode0
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