A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines

Deep learning techniques, such as Deep Boltzmann Machines (DBMs), have received considerable attention over the past years due to the outstanding results concerning a variable range of domains. One of the main shortcomings of these techniques involves the choice of their hyperparameters, since they...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-01
Main Authors: Leandro Aparecido Passos, Papa, João Paulo
Format: Article
Language:English
Subjects:
Heuristic methods
Image reconstruction
Optimization
Optimization techniques
Swarm intelligence
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Deep learning techniques, such as Deep Boltzmann Machines (DBMs), have received considerable attention over the past years due to the outstanding results concerning a variable range of domains. One of the main shortcomings of these techniques involves the choice of their hyperparameters, since they have a significant impact on the final results. This work addresses the issue of fine-tuning hyperparameters of Deep Boltzmann Machines using metaheuristic optimization techniques with different backgrounds, such as swarm intelligence, memory- and evolutionary-based approaches. Experiments conducted in three public datasets for binary image reconstruction showed that metaheuristic techniques can obtain reasonable results.
ISSN:2331-8422
DOI:10.48550/arxiv.2101.05795