Connectionists: [CFP] Special Session - Hypercomplex-valued Neural Networks for Signal Processing (MLSP 2023)

[Marcos Eduardo Valle (IMECC-Unicamp)]

Special Session on Hypercomplex-valued Neural Networks for Signal Processing
<https://2023.ieeemlsp.org/>

33rd IEEE International Workshop on Machine Learning for Signal Processing
(MLSP 2023)

September 17th – 20th 2023 – Fontana di Trevi Conference Centre, Rome, Italy

https://2023.ieeemlsp.org/

Aims and Scope:

Hypercomplex-valued neural networks (HVNNs) constitute a growing research
field that has attracted continued interest for the last decade. Complex
and quaternion-valued neural networks are examples of hypercomplex-valued
models, which also include tessarine and Clifford-valued neural networks.
Complex-valued neural networks are essential for adequately treating angle
and the information contained in phase, including the treatment of wave-
and rotation-related phenomena such as electromagnetism, light waves,
quantum waves, and oscillatory phenomena. Quaternion-valued neural
networks, which have potential applications in three- and four-dimensional
data modeling, have been effectively used to process and analyze
multivariate images such as color and polarimetric SAR images. More
generally, besides their natural ability to treat multidimensional data,
hypercomplex-valued neural networks can benefit from the geometric and
algebraic properties of hypercomplex algebras.

Despite significant theoretical development and successful applications,
there are still many research directions in HVNNs, including a formal
generalization of the commonly used real-valued network architectures and
training algorithms to the hypercomplex-valued case. As powerful machine
learning techniques, there are also many exciting applications of HVNNs for
signal processing, including pattern recognition, nonlinear filtering, and
prediction.

This special session welcomes papers that are or might be related to all
aspects of hypercomplex-valued neural networks, including complex-valued
and quaternion-valued neural networks. Papers on theoretical advances and
contributions of applied nature are all appreciated. We also welcome
interdisciplinary contributions from other areas on the borders of the
proposed scope.

This special session aims to be an excellent forum for exchanging ideas on
HVNNs for signal processing. We hope the proposed session will attract
potential speakers and researchers interested in the interface between the
theory and applications of HVNNs. We also expect this session to benefit
and inspire researchers and practitioners that need sophisticated
machine-learning tools for signal-processing applications.

Organizers:

Marcos Eduardo Valle, Universidade Estadual de Campinas (Unicamp), Brazil.

Guilherme Vieira, Universidade Estadual de Campinas (Unicamp), Brazil.

Akira Hirose, University of Tokyo, Japan.

Danilo Mandic, Imperial College, London, UK.

Igor AizenbergIgor Aizenberg, Manhattan College, USA.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20230412/47e335ed/attachment.html>