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arXiv · 2405.09061

Improving Transformers using Faithful Positional Encoding

Tsuyoshi Idé·Jokin Labaien·Pin-Yu Chen
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Abstract

We propose a new positional encoding method for a neural network architecture called the Transformer. Unlike the standard sinusoidal positional encoding, our approach is based on solid mathematical grounds and has a guarantee of not losing information about the positional order of the input sequence. We show that the new encoding approach systematically improves the prediction performance in the time-series classification task.

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Tsuyoshi Idé, Jokin Labaien, Pin-Yu Chen. 2024-05-15. Improving Transformers using Faithful Positional Encoding. https://arxiv.org/abs/2405.09061

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