با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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Structured pruning of recurrent neural networks through neuron selection
هرس ساختاری شبکه های عصبی مکرر از طریق انتخاب نورون-2020 Recurrent neural networks (RNNs) have recently achieved remarkable successes in a number of
applications. However, the huge sizes and computational burden of these models make it difficult for
their deployment on edge devices. A practically effective approach is to reduce the overall storage and
computation costs of RNNs by network pruning techniques. Despite their successful applications, those
pruning methods based on Lasso either produce irregular sparse patterns in weight matrices, which
is not helpful in practical speedup. To address these issues, we propose a structured pruning method
through neuron selection which can remove the independent neuron of RNNs. More specifically, we
introduce two sets of binary random variables, which can be interpreted as gates or switches to
the input neurons and the hidden neurons, respectively. We demonstrate that the corresponding
optimization problem can be addressed by minimizing the L0 norm of the weight matrix. Finally,
experimental results on language modeling and machine reading comprehension tasks have indicated
the advantages of the proposed method in comparison with state-of-the-art pruning competitors. In
particular, nearly 20× practical speedup during inference was achieved without losing performance
for the language model on the Penn TreeBank dataset, indicating the promising performance of the
proposed method. Keywords: Feature selection | Recurrent neural networks | Learning sparse models | Model compression |
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