Distributed B-SDLM: accelerating the training convergence of deep neural networks through parallelism
This paper proposes an efficient asynchronous stochastic second order learning algorithm for distributed learning of neural networks (NNs). The proposed algorithm, named distributed bounded stochastic diagonal Levenberg-Marquardt (distributed B-SDLM), is based on the B-SDLM algorithm that converges...
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| Format: | Conference or Workshop Item |
| Published: |
Springer Verlag
2016
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