Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features

COLING 2016 Xu Sun

Existing asynchronous parallel learning methods are only for the sparse feature models, and they face new challenges for the dense feature models like neural networks (e.g., LSTM, RNN). The problem for dense features is that asynchronous parallel learning brings gradient errors derived from overwrite actions... (read more)

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