Parallel growing and training of neural networks using output parallelism

@article{Guan2002ParallelGA,
  title={Parallel growing and training of neural networks using output parallelism},
  author={Steven Guan and Shanchun Li},
  journal={IEEE transactions on neural networks},
  year={2002},
  volume={13 3},
  pages={542-50}
}
In order to find an appropriate architecture for a large-scale real-world application automatically and efficiently, a natural method is to divide the original problem into a set of subproblems. In this paper, we propose a simple neural-network task decomposition method based on output parallelism. By using this method, a problem can be divided flexibly into several subproblems as chosen, each of which is composed of the whole input vector and a fraction of the output vector. Each module (for… CONTINUE READING
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