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Article overview
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Simple2Complex: Global Optimization by Gradient Descent | Ming Li
; | Date: |
2 May 2016 | Abstract: | A method named simple2complex for modeling and training deep neural networks
is proposed. Simple2complex train deep neural networks by smoothly adding more
and more layers to the shallow networks, as the learning procedure going on,
the network is just like growing. Compared with learning by end2end,
simple2complex is with less possibility trapping into local minimal, namely,
owning ability for global optimization. Cifar10 is used for verifying the
superiority of simple2complex. | Source: | arXiv, 1605.0404 | Services: | Forum | Review | PDF | Favorites |
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