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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 541–550 of 2050 papers

Title	Date	Tasks	Status	Score
Evaluating Large Language Models as Generative User Simulators for Conversational Recommendation	Mar 13, 2024	Conversational RecommendationModel Selection	CodeCode Available	5
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized Learning	Sep 12, 2024	Ensemble LearningMeta-Learning	CodeCode Available	5
Adaptive Mixtures of Factor Analyzers	Jul 10, 2015	ClusteringDimensionality Reduction	CodeCode Available	5
DeepNNK: Explaining deep models and their generalization using polytope interpolation	Jul 20, 2020	BIG-bench Machine LearningInterpretability Techniques for Deep Learning	CodeCode Available	5
EPP: interpretable score of model predictive power	Aug 24, 2019	Binary ClassificationGeneral Classification	CodeCode Available	5
Evaluating LLP Methods: Challenges and Approaches	Oct 29, 2023	BenchmarkingModel Selection	CodeCode Available	5
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering	Oct 13, 2020	Model SelectionQuestion Answering	CodeCode Available	5
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey	Sep 8, 2019	General ClassificationModel Selection	CodeCode Available	5
Hybrid safe-strong rules for efficient optimization in lasso-type problems	Apr 27, 2017	Model SelectionVocal Bursts Type Prediction	CodeCode Available	5
Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference	Feb 21, 2022	Common Sense ReasoningModel Selection	CodeCode Available	5

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