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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 1011–1020 of 2050 papers

TitleStatusHype
A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engineCode1
Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology—0
Linearised Laplace Inference in Networks with Normalisation Layers and the Neural g-Prior—0
Towards Better Citation Intent Classification—0
UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning—0
On the Use of Entity Embeddings from Pre-Trained Language Models for Knowledge Graph Completion—0
Machine Learning-Assisted Analysis of Small Angle X-ray Scattering—0
Optimizing Unlicensed Coexistence Network Performance Through Data Learning—0
A Rule-Based Epidemiological Modelling Framework—0
Guided Sampling-based Evolutionary Deep Neural Network for Intelligent Fault Diagnosis—0
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