RT journal article
T1 Explainable Hopfield Neural Networks Using an Automatic Video-Generation System
A1 Rubio Manzano, Clemente
A1 Segura-Navarrete, Alejandra
A1 Martinez-Araneda, Claudia
A1 Vidal-Castro, Christian
A2 Matemáticas
K1 explainable artificial intelligence
K1 hopfield neural networks
K1 automatic video generation
K1 data-to-text systems
K1 software visualization
AB Hopfield Neural Networks (HNNs) are recurrent neural networks used to implement associative memory. They can be applied to pattern recognition, optimization, or image segmentation. However, sometimes it is not easy to provide the users with good explanations about the results obtained with them due to mainly the large number of changes in the state of neurons (and their weights) produced during a problem of machine learning. There are currently limited techniques to visualize, verbalize, or abstract HNNs. This paper outlines how we can construct automatic video-generation systems to explain its execution. This work constitutes a novel approach to obtain explainable artificial intelligence systems in general and HNNs in particular building on the theory of data-to-text systems and software visualization approaches. We present a complete methodology to build these kinds of systems. Software architecture is also designed, implemented, and tested. Technical details about the implementation are also detailed and explained. We apply our approach to creating a complete explainer video about the execution of HNNs on a small recognition problem. Finally, several aspects of the videos generated are evaluated (quality, content, motivation and design/presentation).
PB MDPI
SN 2076-3417
YR 2021
FD 2021-07
LK http://hdl.handle.net/10498/25563
UL http://hdl.handle.net/10498/25563
LA eng
NO University of the Bio-Bio. Vicerrectoria de Investigacion. Facultad de Ciencias Empresariales. Departamento de Sistemas de Informacion.
DS Repositorio Institucional de la Universidad de Cádiz
RD 13-nov-2026