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dc.contributor.authorZhabinski, A.-
dc.contributor.authorZhabinskii, S.-
dc.contributor.authorAdzinets, Dz.-
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.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.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
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