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ARC挑战方法的第一步，基于描述性网格模型和最小描述长度原则2021

模型数据数据结构测试对象

First Steps of an Approach to the ARC Challenge based on Descriptive Grid Models and the Minimum Description Length Principle

2024-07-05

990

代数运算对应于认知运算，广义全息缩减表示 GFHRR

数据结构编码框架连接内核

Generalized Holographic Reduced Representations2405.09689v1

2024-07-05

660

矢量符号架构作为纳米级硬件的计算框架

数据结构架构框架算法硬件

Abstract—This article reviews recent progress in the develop- ment of the computing framework Vector Symbolic Architectures(also known as Hyperdimensional Computing). This framework is well suited for implementation in stochastic, nanoscale hard- ware and it naturally expresses the types of cognitive operations required for Artificial Intelligence (AI). We demonstrate in this article that the ring-like algebraic structure of Vector Symbolic Architectures offers simple but powerful operations on high- dimensional vectors that can support all data structures and manipulations relevant in modern computing. In addition, we illustrate the distinguishing feature of Vector Symbolic Archi- tectures, “computing in superposition,” which sets it apart from conventional computing. This latter property opens the door to efficient solutions to the difficult combinatorial search problems inherent in AI applications. Vector Symbolic Architectures are Turing complete, as we show, and we see them acting as a framework for computing with distributed representations in myriad AI settings. This paper serves as a reference for computer architects by illustrating techniques and philosophy of VSAs for distributed computing and relevance to emerging computing hardware, such as neuromorphic computing.

2023-09-01

3600

最新Tractability易处理的因果推理

Causal Inference Using Tractable Circuits

2022-11-22

2740

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