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Simple Recurrent Units for Highly Parallelizable Recurrence

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Figure

Figure 1: Average processing time in milliseconds of a batch of 32 samples using cuDNN LSTM, word-level convolution conv2d (with filter width k = 2 and k = 3), and the proposed SRU
Figure 2: Training curves of SRU on classification.The x-axis is the number of training steps and they-axis is the training loss
Table 1: Test accuracies on classification benchmarks (Section 4.1). The first block presents best reportedresults of various methods
Figure 3: Mean validation accuracies (y-axis) and standard deviations of the CNN, 2-layer LSTM and2-layer SRU models
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