2022/05/19 by Navneet Agrawal, Yuqin Qiu, Agrawal, Navneet +11
Computer Science · Engineering · Physics and Astronomy · #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Neural Networks and Applications #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.2205.09818
openalex publication_date 2022/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Lagrange coded computation (LCC) is essential to solving problems about matrix polynomials in a coded distributed fashion; nevertheless, it can only solve the problems that are representable as matrix polynomials. In this paper, we propose AICC, an AI-aided learning approach that is inspired by LCC but also uses deep neural networks (DNNs). It is appropriate for coded computation of more general functions. Numerical simulations demonstrate the suitability of the proposed approach for the coded computation of different matrix functions that are often utilized in digital signal processing.