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arXiv · 2006.03860

Do RNN and LSTM have Long Memory?

Jingyu Zhao·Feiqing Huang·Jia Lv·Yanjie Duan·Zhen Qin·Guodong Li·Guangjian Tian
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Abstract

The LSTM network was proposed to overcome the difficulty in learning long-term dependence, and has made significant advancements in applications. With its success and drawbacks in mind, this paper raises the question - do RNN and LSTM have long memory? We answer it partially by proving that RNN and LSTM do not have long memory from a statistical perspective. A new definition for long memory networks is further introduced, and it requires the model weights to decay at a polynomial rate. To verify our theory, we convert RNN and LSTM into long memory networks by making a minimal modification, and their superiority is illustrated in modeling long-term dependence of various datasets.

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Jingyu Zhao, Feiqing Huang, Jia Lv, Yanjie Duan, Zhen Qin, Guodong Li, Guangjian Tian. 2020-06-06. Do RNN and LSTM have Long Memory?. https://arxiv.org/abs/2006.03860

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