@InProceedings{PassosJśniorPapa:2017:FiInRe,
author = "Passos J{\'u}nior, Leandro Aparecido and Papa, Jo{\~a}o Paulo",
affiliation = "{Federal University of S{\~a}o Carlos} and {S{\~a}o Paulo State
University}",
title = "Fine-Tuning Infinity Restricted Boltzmann Machines",
booktitle = "Proceedings...",
year = "2017",
editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and
Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and
Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba,
Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo
and Vital, Creto and Pagot, Christian Azambuja and Petronetto,
Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "Deep Learning, Infinity Restricted Boltzmann Machines,
Meta-heuristics.",
abstract = "Restricted Boltzmann Machines (RBMs) have received special
attention in the last decade due to their outstanding results in
number of applications, such as face and human motion recognition,
and collaborative filtering, among others. However, one of the
main concerns about RBMs is related to the number of hidden units,
which is application-dependent. Infinite RBM (iRBM) was proposed
as an alternative to the regular RBM, where the number of units in
the hidden layer grows as long as it is necessary, dropping out
the need for selecting a proper number of hidden units. However, a
less sensitive regularization parameter is introduced as well.
This paper proposes to fine-tune iRBM hyper-parameters by means of
meta-heuristic techniques such as Particle Swarm Optimization, Bat
Algorithm, Cuckoo Search, and the Firefly Algorithm. The proposed
approach is validated in the context of binary image
reconstruction over two well-known datasets. Furthermore, the
experimental results compare the robustness of the iRBM against
the RBM and Ordered RBM (oRBM) using two different learning
algorithms, showing the suitability in using meta-heuristics for
hyper-parameter fine-tuning in RBM-based models.",
conference-location = "Niter{\'o}i, RJ, Brazil",
conference-year = "17-20 Oct. 2017",
doi = "10.1109/SIBGRAPI.2017.15",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.15",
language = "en",
ibi = "8JMKD3MGPAW/3PF4NAB",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PF4NAB",
targetfile = "PID4954803.pdf",
urlaccessdate = "2024, Apr. 29"
}