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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3PF4NAB
Repositorysid.inpe.br/sibgrapi/2017/08.17.04.54
Last Update2017:08.17.04.54.10 administrator
Metadatasid.inpe.br/sibgrapi/2017/08.17.04.54.10
Metadata Last Update2020:02.19.02.01.20 administrator
Citation KeyPassosJúniorPapa:2017:FiInRe
TitleFine-Tuning Infinity Restricted Boltzmann Machines
FormatOn-line
Year2017
Access Date2021, Jan. 26
Number of Files1
Size3011 KiB
Context area
Author1 Passos Júnior, Leandro Aparecido
2 Papa, João Paulo
Affiliation1 Federal University of São Carlos
2 São Paulo State University
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addressleandropassosjr@gmail.com
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
DateOct. 17-20, 2017
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-17 04:54:10 :: leandropassosjr@gmail.com -> administrator ::
2020-02-19 02:01:20 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
KeywordsDeep Learning, Infinity Restricted Boltzmann Machines, Meta-heuristics.
AbstractRestricted 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.
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data URLhttp://urlib.net/rep/8JMKD3MGPAW/3PF4NAB
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PF4NAB
Languageen
Target FilePID4954803.pdf
User Groupleandropassosjr@gmail.com
Visibilityshown
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PJT9LS
8JMKD3MGPAW/3PKCC58
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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