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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 451–460 of 2050 papers

TitleStatusHype
Comprehensive Evaluation of Deep Learning Architectures for Prediction of DNA/RNA Sequence Binding SpecificitiesCode0
All models are wrong, some are useful: Model Selection with Limited LabelsCode0
Nearest Neighbour Equilibrium ClusteringCode0
Neural Architecture Search with Bayesian Optimisation and Optimal TransportCode0
EPP: interpretable score of model predictive powerCode0
A-DARTS: Stable Model Selection for Data Repair in Time SeriesCode0
Conceptually Diverse Base Model Selection for Meta-Learners in Concept Drifting Data StreamsCode0
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological InferenceCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
AALF: Almost Always Linear ForecastingCode0
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