Unbounded Capacity Associative Memory for Real-valued Pattern Storage and Recall

被引：0

作者：

Salem, Fathi M. ^{[1
,2
]}

机构：

[1] Neural AI Syst Inst, Neurai Syst, Okemos, MI 48864 USA

[2] Michigan State Univ, Dept ECE, E Lansing, MI 48824 USA

来源：

2021 IEEE INTERNATIONAL MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS (MWSCAS) | 2021年

关键词：

Real-valued Associate Memory; Deep Learning; Neural Networks; Gradient Systems; (Stochastic) Gradient Descent; Non-convex Optimization;

D O I：

10.1109/MWSCAS47672.2021.9531698

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We describe an unbounded capacity Associative Memory which effectively stores and retrieves unrestricted real-valued data/patterns with fidelity. This Associative Memory arises from a gradient system of a (differentiable scalar) energy function, and thus can directly be incorporated within existing layers of computational Deep Learning (DL) frameworks. The design effort also describes two options for key pattern retrieval.

引用

页码：1066 / 1069

页数：4

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