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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Quaternion-based Deep Belief Networks fine-tuning

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Papa, Joao Paulo [1] ; Rosa, Gustavo H. [1] ; Pereira, Danillo R. [1] ; Yang, Xin-She [2]
Total Authors: 4
[1] Sao Paulo State Univ, Dept Comp, Av Eng Luiz Edmundo Carrijo Coube 14-01, BR-17033360 Bauru, SP - Brazil
[2] Middlesex Univ, Sch Sci & Technol, London NW4 4BT - England
Total Affiliations: 2
Document type: Journal article
Source: APPLIED SOFT COMPUTING; v. 60, p. 328-335, NOV 2017.
Web of Science Citations: 6

Deep learning techniques have been paramount in the last years, mainly due to their outstanding results in a number of applications. In this paper, we address the issue of fine-tuning parameters of Deep Belief Networks by means of meta-heuristics in which real-valued decision variables are described by quaternions. Such approaches essentially perform optimization in fitness landscapes that are mapped to a different representation based on hypercomplex numbers that may generate smoother surfaces. We therefore can map the optimization process onto a new space representation that is more suitable to learning parameters. Also, we proposed two approaches based on Harmony Search and quaternions that outperform the state-of-the-art results obtained so far in three public datasets for the reconstruction of binary images. (C) 2017 Elsevier B.V. All rights reserved. (AU)

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 type: Research Projects - Thematic Grants
FAPESP's process: 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms
Grantee:João Paulo Papa
Support type: Regular Research Grants
FAPESP's process: 15/25739-4 - Ón “The study of semantics ín deep learning models
Grantee:Gustavo Henrique de Rosa
Support type: Scholarships in Brazil - Master