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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...
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| Publicado no: | Entropy (Basel) |
|---|---|
| Main Authors: | , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
MDPI
2018
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| Assuntos: | |
| Acesso em linha: | 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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