Skip navigation
Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/28423
Full metadata record
DC FieldValueLanguage
dc.contributor.authorZhabinski, A.-
dc.contributor.authorZhabinskii, S.-
dc.contributor.authorAdzinets, Dz.-
dc.date.accessioned2017-12-08T11:14:11Z-
dc.date.available2017-12-08T11:14:11Z-
dc.date.issued2017-
dc.identifier.citationZhabinski, A. Symbolic tensor differentiation for applications in machine learning / A. Zhabinski, S. Zhabinskii, Dz. Adzinets // 40 Jubilee International Convention : proceedings (Мaу 22 -26, 2017, Croatia). - Croatia, 2017. – Рр. 338 – 1343. - DOI: 10.17223/1998863Х/34/18.ru_RU
dc.identifier.urihttps://libeldoc.bsuir.by/handle/123456789/28423-
dc.description.abstractAutomated methods for computing derivatives of cost functions are essential to many modern applications of machine learning. Reverse-mode automatic differentiation provides relatively cheap means for it but generated code often requires a lot of memory and is hardly amenable to later optimizations. Symbolic differentiation, on the other hand, generates much more flexible code, yet applying it to multidimensional tensors is a poorly studied topic. In this paper presents a method for symbolic tensor differentiation based on extended Einstein indexing notation, which allows to overcome many limitation of both - automatic and classic symbolic differentiation, and generate efficient code for CPL and GPU.ru_RU
dc.language.isoenru_RU
dc.publisherCroatian Society for Information and Communication Technology, Electronics and Microelectronics MIPRO, Croatiaru_RU
dc.subjectпубликации ученыхru_RU
dc.subjectsymbolic differentiationru_RU
dc.subjectmachine learningru_RU
dc.subjectEinstein notationru_RU
dc.titleSymbolic tensor differentiation for applications in machine learningru_RU
dc.typeСтатьяru_RU
Appears in Collections:Публикации в изданиях других стран

Files in This Item:
File Description SizeFormat 
Zhabinski_Symbolic.pdf873,13 kBAdobe PDFView/Open
Show simple item record


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.