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По вашему запросу найдено документов: 24110

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Automatic code generation for stochastic Runge-Kutta methods

Gevorkyan M.N., Demidova A.V., Korolkova A.V., Kulyabov D.S.

, and the code generation algorithms for numerical schemes are described. Python and Julia languages are used

Issues in the software implementation of stochastic numerical Runge–Kutta

Gevorkyan M.N., Demidova A.V., Korolkova A.V., Kulyabov D.S.

to implement stochastic numerical methods and motivate to use source code generation are described. We discuss

Implementation difficulties analysis of stochastic numerical rungekutta methods

Kulyabov D.S., Gevorkyan M.N., Demidova A.V., Korolkova A.V., Sevastianov L.A., Kotukov M.M.

the approach to the implementation of these methods using source code generation as it allows achieving

Implementation Difficulties Analysis of Stochastic Numerical Runge-Kutta Methods

Gevorkyan M.N., Demidova A.V., Korolkova A.V., Kulyabov D.S., Gostev I.M.

equations. Then we motivate the approach to the implementation of these methods using source code generation

The Semantic Space in Factorial of the Theory of Automatic Generation of Knowledge Architecture (TAGKA-2)

Hardzei, A., Гордей, А. Н.

The Semantic Space in Factorial of the Theory of Automatic Generation of Knowledge Architecture

Автоматическая генерация хэш-функций для обфускации программного кода

Лебедев, Роман Константинович

Automatic generation of hash functions for program code obfuscation

Онтологический подход к автоматической генерации вопросов в интеллектуальных обучающих системах

Ли, Вэньцзу, Li, Wenzu

of automatic generation of questions in intelligent learning systems. The designed subsystem allows various

Evaluation methods for code generation models

Zhong Wu, Rybak, V. A.

This article discusses evaluation methods for code generation models, focusing on BLEU scores

Codec for two-dimensional hamming code on FPGA

Chen, Y. M.

selectable features such as hybrid automatic repeat request and parallel coding.

Symbolic tensor differentiation for applications in machine learning

Zhabinski, A., Zhabinskii, S., Adzinets, D. N., Жабинский, А. В., Жабинский, С. В., Одинец, Д. Н.

for it but generated code often requires a lot of memory and is hardly amenable to later optimizations. Symbolic

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