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Found 5 papers, 1 papers with code
Universal Approximation Theorem for Vector- and Hypercomplex-Valued Neural Networks
However, hypercomplex-valued neural networks are a type of vector-valued neural network defined on an algebra with additional algebraic or geometric properties.
Dual Quaternion Rotational and Translational Equivariance in 3D Rigid Motion Modelling
To overcome these limitations, we employ a dual quaternion representation of rigid motions in the 3D space that jointly describes rotations and translations of point sets, processing each of the points as a single entity.
Extending the Universal Approximation Theorem for a Broad Class of Hypercomplex-Valued Neural Networks
The universal approximation theorem asserts that a single hidden layer neural network approximates continuous functions with any desired precision on compact sets.
Acute Lymphoblastic Leukemia Detection Using Hypercomplex-Valued Convolutional Neural Networks
This paper features convolutional neural networks defined on hypercomplex algebras applied to classify lymphocytes in blood smear digital microscopic images.
A General Framework for Hypercomplex-valued Extreme Learning Machines
This paper aims to establish a framework for extreme learning machines (ELMs) on general hypercomplex algebras.
