Advanced search
Start date
Betweenand
Related content
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Reinforcing learning in Deep Belief Networks through nature-inspired optimization

Full text
Author(s):
Roder, Mateus [1] ; Passos, Leandro Aparecido [1] ; de Rosa, Gustavo H. [1] ; de Albuquerque, Victor Hugo C. [2, 3] ; Papa, Joao Paulo [1]
Total Authors: 5
Affiliation:
[1] Sao Paulo State Univ, Dept Comp, Av Engn Luiz Edmundo Carrijo Coube 14-01, BR-17033360 Bauru, SP - Brazil
[2] Univ Fed Ceara, Grad Program Teleinformat Engn, Fortaleza, CE - Brazil
[3] Fed Inst Educ Sci & Technol Ceara, Grad Program Telecommun Engn, Fortaleza, CE - Brazil
Total Affiliations: 3
Document type: Journal article
Source: APPLIED SOFT COMPUTING; v. 108, SEP 2021.
Web of Science Citations: 0
Abstract

Deep learning techniques usually face drawbacks related to the vanishing gradient problem, i.e., the gradient becomes gradually weaker when propagating from one layer to another until it finally vanishes away and no longer helps in the learning process. Works have addressed this problem by introducing residual connections, thus assisting gradient propagation. However, such a subject of study has been poorly considered for Deep Belief Networks. In this paper, we propose a weighted layer-wise information reinforcement approach concerning Deep Belief Networks. Moreover, we also introduce metaheuristic optimization to select proper weight connections that improve the network's learning capabilities. Experiments conducted over public datasets corroborate the effectiveness of the proposed approach in image classification tasks. (C) 2021 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 19/07825-1 - Deep Boltzmann machines for event recognition in videos
Grantee:Mateus Roder
Support Opportunities: Scholarships in Brazil - Master
FAPESP's process: 18/21934-5 - Network statistics: theory, methods, and applications
Grantee:André Fujita
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 17/25908-6 - Weakly supervised learning for compressed video analysis on retrieval and classification tasks for visual alert
Grantee:João Paulo Papa
Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 19/07665-4 - Center for Artificial Intelligence
Grantee:Fabio Gagliardi Cozman
Support Opportunities: Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program
FAPESP's process: 19/02205-5 - Adversarial learning in natural language processing
Grantee:Gustavo Henrique de Rosa
Support Opportunities: Scholarships in Brazil - Doctorate
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:Francisco Louzada Neto
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
Grantee:Alexandre Xavier Falcão
Support Opportunities: Research Projects - Thematic Grants