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On the Reduction of Computational Complexity of Deep Convolutional Neural Networks †
Deep convolutional neural networks (ConvNets), which are at the heart of many new emerging applications, achieve remarkable performance in audio and visual recognition tasks. Unfortunately, achieving accuracy often implies significant computational costs, limiting deployability. In modern ConvNets i...
Bewaard in:
| Gepubliceerd in: | Entropy (Basel) |
|---|---|
| Hoofdauteurs: | , |
| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
MDPI
2018
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| Onderwerpen: | |
| Online toegang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7512823/ https://ncbi.nlm.nih.gov/pubmed/33265396 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/e20040305 |
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