相关实验视频
Updated: Jul 6, 2025

Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
计算通用卷积比原始力更快
Barış Can Esmer1, Ariel Kulik1, Dániel Marx1
1CISPA Helmholtz Center for Information Security, Saarbrücken, Germany.
我们介绍了一个通用的卷积,f-卷积,适用于各种离散结构. 我们的研究提供了一个高效的算法来计算f-Convolution在多项式时间,显著改进了天真的方法.
科学领域:
- 离散数学 离散数学 离散数学
- 理论计算机科学 理论计算机科学
- 算法分析 算法分析
背景情况:
- 介绍了一个通用的卷积运算,称为f-Convolution,定义在有限的域和向量空间上.
- 这个操作统一和扩展了几个已知的卷积类型,包括子集卷积,XOR产品,覆盖产品和包装产品.
- f-Convolution 的天真粗暴力计算的时间复杂度为 O ((n^d),其中 d 是域大小.
研究的目的:
- 开发一种更有效的算法来计算一般化的f-Convolution.
- 分析拟议的算法的计算复杂性,并将其与天真方法进行比较.
- 为了介绍和解决f-Query问题,对直角向量问题的概括.
主要方法:
- 提出了一个精确的计算f-Convolution在O(n^(d/2)) 时间的常数d当域有偶数的枢纽.
- 使用一种涉及函数循环分区的新技术来加速卷积计算.
- 开发了一个O(n^d * M(n^(1-1/d))) 算法用于f-Query问题,其中M(k) 是k x k矩阵乘法的时间.
主要成果:
- 在f-Convolution计算中比原始力方法取得了显著的非对称改进.
- 证明任何函数 f. 存在合适的循环分区.
- 为 f-Query 问题提供了一个有效的解决方案,将直角向量问题概括起来.
结论:
- 拟议的循环分区方法为计算通用卷积提供了相当大的加速.
- 可以有效地解决f-Query问题,其复杂性与最先进的矩阵乘法有关.
- 这项工作为有效解决理论计算机科学中涉及一般化卷积运算的问题开辟了新的途径.
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