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|a 10.1109/TPAMI.2024.3378843
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|a eng
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|a Shen, Yanting
|e verfasserin
|4 aut
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|a AutoNet-Generated Deep Layer-Wise Convex Networks for ECG Classification
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|a Date Revised 09.09.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a The design of neural networks typically involves trial-and-error, a time-consuming process for obtaining an optimal architecture, even for experienced researchers. Additionally, it is widely accepted that loss functions of deep neural networks are generally non-convex with respect to the parameters to be optimised. We propose the Layer-wise Convex Theorem to ensure that the loss is convex with respect to the parameters of a given layer, achieved by constraining each layer to be an overdetermined system of non-linear equations. Based on this theorem, we developed an end-to-end algorithm (the AutoNet) to automatically generate layer-wise convex networks (LCNs) for any given training set. We then demonstrate the performance of the AutoNet-generated LCNs (AutoNet-LCNs) compared to state-of-the-art models on three electrocardiogram (ECG) classification benchmark datasets, with further validation on two non-ECG benchmark datasets for more general tasks. The AutoNet-LCN was able to find networks customised for each dataset without manual fine-tuning under 2 GPU-hours, and the resulting networks outperformed the state-of-the-art models with fewer than 5% parameters on all the above five benchmark datasets. The efficiency and robustness of the AutoNet-LCN markedly reduce model discovery costs and enable efficient training of deep learning models in resource-constrained settings
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|a Journal Article
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|a Lu, Lei
|e verfasserin
|4 aut
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| 700 |
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|a Zhu, Tingting
|e verfasserin
|4 aut
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| 700 |
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|a Wang, Xinshao
|e verfasserin
|4 aut
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| 700 |
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|a Clifton, Lei
|e verfasserin
|4 aut
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| 700 |
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|a Chen, Zhengming
|e verfasserin
|4 aut
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| 700 |
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|a Clarke, Robert
|e verfasserin
|4 aut
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| 700 |
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|a Clifton, David A
|e verfasserin
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 46(2024), 10 vom: 09. Sept., Seite 6542-6558
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|x 1939-3539
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|g volume:46
|g year:2024
|g number:10
|g day:09
|g month:09
|g pages:6542-6558
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|u http://dx.doi.org/10.1109/TPAMI.2024.3378843
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