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EXPLAINABLE HOPFIELD NEURAL NETWORKS USING AN AUTOMATIC VIDEO-GENERATION SYSTEM

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dc.creatorCLEMENTE RUBIO MANZANO
dc.creatorALEJANDRA ANDREA SEGURA NAVARRETE
dc.creatorCHRISTIAN LAUTARO VIDAL CASTRO
dc.date2021
dc.date.accessioned2025-01-10T15:21:59Z
dc.date.available2025-01-10T15:21:59Z
dc.date.issued2021
dc.description.abstractHOPFIELD NEURAL NETWORKS (HNNS) ARE RECURRENT NEURAL NETWORKS USED TO IMPLEMENT ASSOCIATIVE MEMORY. THEY CAN BE APPLIED TO PATTERN RECOGNITION, OPTIMIZATION, OR IMAGE SEGMENTATION. HOWEVER, SOMETIMES IT IS NOT EASY TO PROVIDE THE USERS WITH GOOD EXPLANATIONS ABOUT THE RESULTS OBTAINED WITH THEM DUE TO MAINLY THE LARGE NUMBER OF CHANGES IN THE STATE OF NEURONS (AND THEIR WEIGHTS) PRODUCED DURING A PROBLEM OF MACHINE LEARNING. THERE ARE CURRENTLY LIMITED TECHNIQUES TO VISUALIZE, VERBALIZE, OR ABSTRACT HNNS. THIS PAPER OUTLINES HOW WE CAN CONSTRUCT AUTOMATIC VIDEO-GENERATION SYSTEMS TO EXPLAIN ITS EXECUTION. THIS WORK CONSTITUTES A NOVEL APPROACH TO OBTAIN EXPLAINABLE ARTIFICIAL INTELLIGENCE SYSTEMS IN GENERAL AND HNNS IN PARTICULAR BUILDING ON THE THEORY OF DATA-TO-TEXT SYSTEMS AND SOFTWARE VISUALIZATION APPROACHES. WE PRESENT A COMPLETE METHODOLOGY TO BUILD THESE KINDS OF SYSTEMS. SOFTWARE ARCHITECTURE IS ALSO DESIGNED, IMPLEMENTED, AND TESTED. TECHNICAL DETAILS ABOUT THE IMPLEMENTATION ARE ALSO DETAILED AND EXPLAINED. WE APPLY OUR APPROACH TO CREATING A COMPLETE EXPLAINER VIDEO ABOUT THE EXECUTION OF HNNS ON A SMALL RECOGNITION PROBLEM. FINALLY, SEVERAL ASPECTS OF THE VIDEOS GENERATED ARE EVALUATED (QUALITY, CONTENT, MOTIVATION AND DESIGN/PRESENTATION).
dc.formatapplication/pdf
dc.identifier.doi10.3390/app11135771
dc.identifier.issn2076-3417
dc.identifier.issn2076-3417
dc.identifier.urihttps://repositorio.ubiobio.cl/handle/123456789/11673
dc.languagespa
dc.publisherApplied Sciences-Basel
dc.relation.uri10.3390/app11135771
dc.rightsOPEN ACCESS
dc.subjectSoftware visualization
dc.subjectData-to-text systems
dc.subjectAutomatic video generation
dc.subjectHopfield neural networks
dc.subjectExplainable artificial intelligence
dc.titleEXPLAINABLE HOPFIELD NEURAL NETWORKS USING AN AUTOMATIC VIDEO-GENERATION SYSTEM
dc.typeARTÍCULO
dspace.entity.typePublication
oaire.licenseConditionCC BY 4.0
ubb.EstadoPUBLICADA
ubb.Otra ReparticionDEPARTAMENTO DE SISTEMAS DE INFORMACION
ubb.Otra ReparticionDEPARTAMENTO DE SISTEMAS DE INFORMACION
ubb.Otra ReparticionDEPARTAMENTO DE SISTEMAS DE INFORMACION
ubb.SedeCONCEPCIÓN
ubb.SedeCONCEPCIÓN
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