تقرير
Efficient and Sound Differentiable Programming in a Functional Array-Processing Language
| العنوان: | Efficient and Sound Differentiable Programming in a Functional Array-Processing Language |
|---|---|
| المؤلفون: | Shaikhha, Amir, Huot, Mathieu, Ghasemirad, Shabnam, Fitzgibbon, Andrew, Jones, Simon Peyton, Vytiniotis, Dimitrios |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Programming Languages, Computer Science - Machine Learning, Computer Science - Mathematical Software |
| الوصف: | Automatic differentiation (AD) is a technique for computing the derivative of a function represented by a program. This technique is considered as the de-facto standard for computing the differentiation in many machine learning and optimisation software tools. Despite the practicality of this technique, the performance of the differentiated programs, especially for functional languages and in the presence of vectors, is suboptimal. We present an AD system for a higher-order functional array-processing language. The core functional language underlying this system simultaneously supports both source-to-source forward-mode AD and global optimisations such as loop transformations. In combination, gradient computation with forward-mode AD can be as efficient as reverse mode, and the Jacobian matrices required for numerical algorithms such as Gauss-Newton and Levenberg-Marquardt can be efficiently computed. Comment: arXiv admin note: substantial text overlap with arXiv:1806.02136 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2212.10307 |
| رقم الأكسشن: | edsarx.2212.10307 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |