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A comparison of geometric analogues of holographic reduced representations, original holographic reduced representations and binary spatter codes

机译:全息简化表示,原始全息简化表示和二进制飞溅代码的几何类似物的比较

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Geometric Analogues of Holographic Reduced Representations (GA HRR) employ role-filler binding based on geometric products. Atomic objects are real-valued vectors in n-dimensional Euclidean space and complex statements belong to a hierarchy of multivectors. The paper reports a battery of tests aimed at comparison of GA HRR with Holographic Reduced Representation (HRR) and Binary Spatter Codes (BSC). Firstly, we perform a test of GA HRR which is analogous to the one proposed by Plate in [13]. Plate''s simulation involved several thousand 512-dimensional vectors stored in clean-up memory. The purpose was to study efficiency of HRR but also to provide a counterexample to claims that role-filler representations do not permit one component of a relation to be retrieved given the others. We repeat Plate''s test on a continuous version of GA HRR — GAc (as opposed to its discrete version described in [12]) and compare the results with the original HRR and BSC. The object of the test is to construct statements concerning multiplication and addition. For example, “2·3 = 6” is constructed as times2,3 = times+operand∗(num2 + num3)+result∗num6. To look up this vector one then constructs a similar statement with one of the components missing and checks whether it points correctly to times2,3. We concentrate on comparison of recognition percentage for the three models for comparable data size, rather than on the time taken to achieve high percentage. Results show that the best models for storing and recognizing multiple similar statements are GAc and Binary Spatter Codes with recognition percentage highly above 90.
机译:全息缩图表示的几何类似物(GA HRR)采用基于几何积的角色填充器绑定。原子对象是n维欧式空间中的实值向量,复杂语句属于多向量的层次结构。该论文报告了一系列测试,旨在将GA HRR与全息缩小表示(HRR)和二进位飞溅代码(BSC)进行比较。首先,我们进行了GA HRR的测试,类似于Plate在[13]中提出的测试。 Plate的仿真涉及清理内存中存储的数千个512维向量。目的是研究HRR的效率,但也提供一个反例,以证明角色扮演者表示不允许在给定其他组件的情况下检索到关系的一个组件。我们在连续版本的GA HRR — GA c (与[12]中描述的离散版本相反)上重复Plate的测试,并将结果与​​原始HRR和BSC进行比较。测试的目的是构造有关乘法和加法的语句。例如,“ 2·3 = 6”被构造为times 2,3 = times + operand *(num 2 + num 3 ) +结果* num 6 。为了查找该向量,然后构造一个相似的语句,其中缺少一个分量,并检查它是否正确指向times 2,3 。我们专注于比较三个模型在可比较的数据大小上的识别百分比,而不是集中在获得高百分比所花费的时间上。结果表明,用于存储和识别多个相似语句的最佳模型是GA c 和Binary Spatter代码,其识别百分比都大大高于90。

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