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  • Author(s):
    Kenton Murray, David Chiang

    Neural networks have been shown to improve performance across a range of natural-language tasks. However, designing and training them can be complicated. Frequently, researchers resort to repeated experimentation to pick optimal settings. In this paper, we address the issue of choosing the correct number of units in hidden layers. We introduce a method for automatically adjusting network size by pruning out hidden units through L∞,1 and L2,1 regularization. We apply this method to language mo…