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Dynamically Optimizing Network Structure Based on Synaptic Pruning in the Brain
Most neural networks need to predefine the network architecture empirically, which may cause over-fitting or under-fitting. Besides, a large number of parameters in a fully connected network leads to the prohibitively expensive computational cost and storage overhead, which makes the model hard to b...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Frontiers Media S.A.
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8220807/ https://www.ncbi.nlm.nih.gov/pubmed/34177473 http://dx.doi.org/10.3389/fnsys.2021.620558 |