December 2019

Thesis defense of Titouan Parcollet, entitled “Artificial Neural Networks Based on Quaternion Algebra,” will take place on Tuesday, December 3, 2019, at 2:30 PM in the Blaise Pascal amphitheater (CERI). The thesis will be presented before a jury composed of: The defense will be conducted in French. You are also invited to the reception following the defense in Room 5. Abstract: In recent years, deep learning has become the preferred approach for developing modern artificial intelligence (AI). The significant increase in computing power, along with the ever-growing amount of available data, has made deep neural networks the most efficient solution for solving complex problems. However, accurately representing the multidimensionality of real-world data remains a major challenge for artificial neural architectures. To address this challenge, neural networks based on complex and hypercomplex number algebras have been developed. Thus, the multidimensionality of data is integrated into neurons, which are now complex and hypercomplex components of the model. In particular, quaternion neural networks (QNNs) have been proposed to process three-dimensional and four-dimensional data, based on quaternions representing rotations in our three-dimensional space. Unfortunately, unlike complex-valued neural networks, which are now accepted as an alternative to real-valued neural networks, QNNs suffer from several limitations, Plus d'infos