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|a 10.1007/s10462-024-10943-8
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|a eng
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1 |
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|a Cueto-Mendoza, Eduardo
|e verfasserin
|4 aut
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| 245 |
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|a A framework for measuring the training efficiency of a neural architecture
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|c 2024
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 01.11.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © The Author(s) 2024.
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|a Measuring Efficiency in neural network system development is an open research problem. This paper presents an experimental framework to measure the training efficiency of a neural architecture. To demonstrate our approach, we analyze the training efficiency of Convolutional Neural Networks and Bayesian equivalents on the MNIST and CIFAR-10 tasks. Our results show that training efficiency decays as training progresses and varies across different stopping criteria for a given neural model and learning task. We also find a non-linear relationship between training stopping criteria, training Efficiency, model size, and training Efficiency. Furthermore, we illustrate the potential confounding effects of overtraining on measuring the training efficiency of a neural architecture. Regarding relative training efficiency across different architectures, our results indicate that CNNs are more efficient than BCNNs on both datasets. More generally, as a learning task becomes more complex, the relative difference in training efficiency between different architectures becomes more pronounced
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| 650 |
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|a Journal Article
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|a Deep learning
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| 650 |
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|a Deep neural networks
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| 650 |
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4 |
|a Efficiency
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| 650 |
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4 |
|a Hyperparameters
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| 700 |
1 |
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|a Kelleher, John
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Artificial intelligence review
|d 1998
|g 57(2024), 12 vom: 23., Seite 349
|w (DE-627)NLM098184490
|x 0269-2821
|7 nnns
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| 773 |
1 |
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|g volume:57
|g year:2024
|g number:12
|g day:23
|g pages:349
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|u http://dx.doi.org/10.1007/s10462-024-10943-8
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