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JACIII Vol.23 No.1 pp. 60-66
doi: 10.20965/jaciii.2019.p0060
(2019)

Paper:

Analog Value Associative Memory Using Restricted Boltzmann Machine

Yuichiro Tsutsui and Masafumi Hagiwara

Department of Information and Computer Science, Keio University
3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan

Corresponding author

Received:
March 9, 2018
Accepted:
October 22, 2018
Published:
January 20, 2019
Keywords:
semantic network, Restricted Boltzmann Machine, word2vec, associative memory
Abstract
Analog Value Associative Memory Using Restricted Boltzmann Machine

Structure of the proposed analog value associative memory using RBM (AVAM)

In this paper, we propose an analog value associative memory using Restricted Boltzmann Machine (AVAM). Research on treating knowledge is becoming more and more important such as in natural language processing and computer vision fields. Associative memory plays an important role to store knowledge. First, we obtain distributed representation of words with analog values using word2vec. Then the obtained distributed representation is learned in the proposed AVAM. In the evaluation experiments, we found simple but very important phenomenon in word2vec method: almost all of the values in the generated vectors are small values. By applying traditional normalization method for each word vector, the performance of the proposed AVAM is largely improved. Detailed experimental evaluations are carried out to show superior performance of the proposed AVAM.

Cite this article as:
Y. Tsutsui and M. Hagiwara, “Analog Value Associative Memory Using Restricted Boltzmann Machine,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.1, pp. 60-66, 2019.
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Last updated on Feb. 22, 2019