文件名称:
Training and Analyzing Deep Recurrent Neural Networks
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文件大小: 333kb
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上传时间: 2018-07-22
详细说明： Abstract Time series often have a temporal hierarchy, with information that is spread out over multiple time scales. Common recurrent neural networks, however, do not explicitly accommodate such a hierarchy, and most research on them has been focusing on training algorithms rather than on their basic architecture. In this pa- per we study the effect of a hierarchy of recurrent neural networks on processing time series. Here, each layer is a recurrent network which receives the hidden state of the previous layer as input. This ar chitecture allows us to perform hi- erarchical processing on difficult temporal tasks, and more naturally capture the structure of time series. We show that they reach state-of-the-art performance for recurrent networks in character-level language modeling when trained with sim- ple stochastic gradient descent. We also offer an analysis of the different emergent time scales. ...展开收缩
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