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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 1871–1880 of 2050 papers

TitleStatusHype
Deep Learning and Linear Programming for Automated Ensemble Forecasting and InterpretationCode0
INFaaS: A Model-less and Managed Inference Serving SystemCode0
Neural Vector Spaces for Unsupervised Information RetrievalCode0
Reliable Time Prediction in the Markov Stochastic Block ModelCode0
Comparative Evaluation of Learning Models for Bionic Robots: Non-Linear Transfer Function IdentificationsCode0
Inferring Convolutional Neural Networks' accuracies from their architectural characterizationsCode0
Framework for Inferring Following Strategies from Time Series of Movement DataCode0
Comparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing DataCode0
Combining UPerNet and ConvNeXt for Contrails Identification to reduce Global WarmingCode0
Infinite Action Contextual Bandits with Reusable Data ExhaustCode0
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