在高维表示中使用余数计算
Christopher J Kymn1, Denis Kleyko2,3, E Paxon Frady4
1Redwood Center for Theoretical Neuroscience, University of California, Berkeley, CA.
ArXiv
|November 21, 2023
概括
我们介绍了残余超维计算,这是一个新的框架,结合了残余数字系统和高维向量. 这种方法高效地处理大数值范围与噪声强度,提供新的机器学习和神经科学见解.
科学领域:
- 计算机科学 计算机科学
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 传统的计算与大动态范围和噪声作斗争.
- 剩余数系统 (RNS) 在某些算术运算中提供了优势.
- 超维计算 (HDC) 使用高维向量进行强大的数据表示.
研究的目的:
- 引入剩余高维计算 (Res-HDC),一个统一的计算框架.
- 为了证明Res-HDC在表示和运行数值方面的效率.
- 探索Res-HDC在解决复杂计算问题和建模神经计算方面的潜力.
主要方法:
- 以高维向量表示残留数.
- 通过组件智能,可并行的矢量运算来执行代数运算.
- 使用高维向量的高效因子化方法.
主要成果:
- Res-HDC使大动态范围的操作能够使用更少的资源.
- 框架在噪音的情况下表现出强大的性能.
- 与基线方法相比,在视觉感知和组合优化任务方面取得了明显的改进.
结论:
- 剩余超维计算提供了一个资源高效和噪声强大的计算范式.
- 该框架在机器学习架构和理解大脑计算,特别是网格细胞操作方面具有潜在的应用.
- 这种统一的方法为数值数据表示和操纵开辟了新的途径.
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