@inproceedings{7e290bd4999048b8b18d0aaaff0f127c,
title = "Bayesian Optimization for Sparse Artificial Neural Networks: Application to Change Detection in Remote Sensing",
abstract = "Artificial neural networks (ANNs) are today the most popular machine learning algorithms. ANNs are widely applied in various fields such as medical imaging and remote sensing. One of the main challenges related to the use of ANNs is the inherent optimization problem to be solved during the training phase. This optimization step is generally performed using a gradient-based approach with a backpropagation strategy. For the sake of efficiency, regularization is generally used. When non-smooth regularizers are used to promote sparse networks, this optimization becomes challenging. Classical gradient-based optimizers cannot be used due to differentiability issues. In this paper, we propose an efficient optimization scheme formulated in a Bayesian framework. Hamiltonian dynamics are used to design an efficient sampling scheme. Promising results show the usefulness of the proposed method to allow ANNs with low complexity levels reaching high accuracy rates while performing faster that with other optimizers.",
keywords = "Artificial neural networks, Deep learning, Hamiltonian dynamics, Machine learning, MCMC, Optimization",
author = "Mohamed Fakhfakh and Bassem Bouaziz and Hadj Batatia and Lotfi Chaari",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 15th International Conference on Information Technology and Applications 2021, ICITA 2021 ; Conference date: 13-11-2021 Through 14-11-2021",
year = "2022",
month = apr,
day = "21",
doi = "10.1007/978-981-16-7618-5_4",
language = "English",
isbn = "9789811676178",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer",
pages = "39--49",
editor = "Abrar Ullah and Steve Gill and {\'A}lvaro Rocha and Sajid Anwar",
booktitle = "Proceedings of International Conference on Information Technology and Applications. ICITA 2021",
}