Time-warping transfer learning adapts an RNN trained on 10h fuel moisture data to predict for other fuel classes with different lag times, validated on Oklahoma field study data.
A Survey on Transfer Learning , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
Optimizes ImageNet-pretrained AlexNet, UMAP, and a bagging multi-cluster voting scheme with K-means, Birch and Agg for unsupervised galaxy morphology classification, reporting improved stability and consistency with galaxy evolution expectations.
citing papers explorer
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Transfer Learning for Dead Fuel Moisture Prediction Using Time-Warping Recurrent Neural Networks
Time-warping transfer learning adapts an RNN trained on 10h fuel moisture data to predict for other fuel classes with different lag times, validated on Oklahoma field study data.
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Robustness Analysis of USmorph: II. Optimizing Feature Extraction, Dimensionality Reduction, and Clustering for Unsupervised Galaxy Morphology Classification
Optimizes ImageNet-pretrained AlexNet, UMAP, and a bagging multi-cluster voting scheme with K-means, Birch and Agg for unsupervised galaxy morphology classification, reporting improved stability and consistency with galaxy evolution expectations.