论文标题
波浪之间的翻译，波2波
Translation Between Waves, wave2wave
论文作者
论文摘要
通过大数据的先进深度学习方法，对传感器数据的理解得到了极大的提高。但是，现实世界中的可用传感器数据仍然有限，这称为机会主义传感器问题。本文提出了一种新的神经机器翻译SEQ2SEQ的新变体，以通过引入基于窗口的（逆）表示来应对连续信号波，以适应性地表示波的部分形状和迭代的反向翻译模型，以实现高维数据。显示了两个现实生活数据的实验结果：地震和活性翻译。一维数据的性能改善约为46％的测试损失，与原始SEQ2SEQ相比，高维数据的困惑约为1625％。
The understanding of sensor data has been greatly improved by advanced deep learning methods with big data. However, available sensor data in the real world are still limited, which is called the opportunistic sensor problem. This paper proposes a new variant of neural machine translation seq2seq to deal with continuous signal waves by introducing the window-based (inverse-) representation to adaptively represent partial shapes of waves and the iterative back-translation model for high-dimensional data. Experimental results are shown for two real-life data: earthquake and activity translation. The performance improvements of one-dimensional data was about 46% in test loss and that of high-dimensional data was about 1625% in perplexity with regard to the original seq2seq.
