2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP) 2023
DOI: 10.1109/mlsp55844.2023.10285926
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Phydi: Initializing Parameterized Hypercomplex Neural Networks As Identity Functions

Matteo Mancanelli,
Eleonora Grassucci,
Aurelio Uncini
et al.

Abstract: Neural models based on hypercomplex algebra systems are growing and prolificating for a plethora of applications, ranging from computer vision to natural language processing. Hand in hand with their adoption, parameterized hypercomplex neural networks (PHNNs) are growing in size and no techniques have been adopted so far to control their convergence at a large scale. In this paper, we study PHNNs convergence and propose parameterized hypercomplex identity initialization (PHYDI), a method to improve their conve… Show more

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