RNN Architecture Learning with Sparse Regularization

EMNLP/IJCNLP (1), pp. 1179-1184, 2019.

Cited by: 4|Views60
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Abstract:

Neural models for NLP typically use large numbers of parameters to reach state-of-the-art performance, which can lead to excessive memory usage and increased runtime. We present a structure learning method for learning sparse, parameter-efficient NLP models. Our method applies group lasso to rational RNNs (Peng et al., 2018), a family o...More

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