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Building Blocks for a Complex-Valued Transformer Architecture

Citation Author(s):
Florian Eilers, Xiaoyi Jiang
Submitted by:
Florian Eilers
Last updated:
17 November 2023 - 12:07pm
Document Type:
Research Manuscript
Document Year:
Florian Eilers
Paper Code:

Most deep learning pipelines are built on real-valued operations to deal with real-valued inputs such as images, speech or music signals. However, a lot of applications naturally make use of complex-valued signals or images, such as MRI or remote sensing. Additionally the Fourier transform of signals is complex-valued and has numerous applications. We aim to make deep learning directly applicable to these complex-valued signals without using projections into R2 . Thus we add to the recent developments of complex-valued neural networks by presenting building blocks to transfer the transformer architecture to the complex domain. We present multiple versions of a complex-valued Scaled Dot-Product Attention mechanism as well as a complex-valued layer normalization. We test on a classification and a sequence generation task on the MusicNet dataset and show improved robustness to overfitting while maintaining on-par performance when compared to the real-valued transformer architecture.

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