Hyperparameters, tuning spaces, and the best hyperparameter settings for deep learning methods (Ct-Transformer, TFT, Transformer, and MLP) on the ER dataset and SF dataset.

<p>Hyperparameters, tuning spaces, and the best hyperparameter settings for deep learning methods (Ct-Transformer, TFT, Transformer, and MLP) on the ER dataset and SF dataset.</p>

Publication Details

Journal
Figshare
Published
2024-12-23
DOI
https://doi.org/10.1371/journal.pcbi.1012694.s011
Primary Topic
Wheat and Barley Genetics and Pathology
Type
article
Field-Weighted Citation Impact
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article

Hyperparameters, tuning spaces, and the best hyperparameter settings for deep learning methods (Ct-Transformer, TFT, Transformer, and MLP) on the ER dataset and SF dataset.

Figshare
Wheat and Barley Genetics and Pathology
article

Hyperparameters, tuning spaces, and the best hyperparameter settings for deep learning methods (Ct-Transformer, TFT, Transformer, and MLP) on the ER dataset and SF dataset.

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Abstract

<p>Hyperparameters, tuning spaces, and the best hyperparameter settings for deep learning methods (Ct-Transformer, TFT, Transformer, and MLP) on the ER dataset and SF dataset.</p>

Figshare
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Wheat and Barley Genetics and Pathology
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Hyperparameters, tuning spaces, and the best hyperparameter settings for deep learning methods (Ct-Transformer, TFT, Transformer, and MLP) on the ER dataset and SF dataset. · Figshare (2024) | TGRS Research Map | TGRS