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arXiv · 2307.11462

Improve Long-term Memory Learning Through Rescaling the Error Temporally

Shida Wang·Zhanglu Yan
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Abstract

This paper studies the error metric selection for long-term memory learning in sequence modelling. We examine the bias towards short-term memory in commonly used errors, including mean absolute/squared error. Our findings show that all temporally positive-weighted errors are biased towards short-term memory in learning linear functionals. To reduce this bias and improve long-term memory learning, we propose the use of a temporally rescaled error. In addition to reducing the bias towards short-term memory, this approach can also alleviate the vanishing gradient issue. We conduct numerical experiments on different long-memory tasks and sequence models to validate our claims. Numerical results confirm the importance of appropriate temporally rescaled error for effective long-term memory learning. To the best of our knowledge, this is the first work that quantitatively analyzes different errors' memory bias towards short-term memory in sequence modelling.

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Shida Wang, Zhanglu Yan. 2023-07-21. Improve Long-term Memory Learning Through Rescaling the Error Temporally. https://arxiv.org/abs/2307.11462

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