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Title:
TRAINING DEVICE, ESTIMATION DEVICE, TRAINING METHOD, ESTIMATION METHOD, AND PROGRAM
Document Type and Number:
WIPO Patent Application WO/2020/217620
Kind Code:
A1
Abstract:
An objective of the present invention is to train a high-precision graph generation algorithm. The training device comprises one or a plurality of memories and one or a plurality of processors. The one or the plurality of processors train a first converter for transforming a first feature value relating to a node of a graph and a second feature value relating to a structure of the graph into a first latent value by a transformation whereby an inverse transformation can be defined, and a second converter which transforms the second feature value into a second latent value by a transformation whereby an inverse transformation can be defined, the first converter and the second converter being trained on the basis of the first latent value and the second latent value.

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Inventors:
PITUWALAKANKANAMGE KAUSHALYA MADHAWA (JP)
NAKAGO KOSUKE (JP)
ISHIGURO KATSUHIKO (JP)
Application Number:
PCT/JP2020/003052
Publication Date:
October 29, 2020
Filing Date:
January 28, 2020
Export Citation:
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Assignee:
PREFERRED NETWORKS INC (JP)
International Classes:
G06N20/00
Other References:
JEPSEN, TOBIAS SKOVGAARD: "How to do Deep Learning on Graphs with Graph Convolutional Networks", PART 1: A HIGH-LEVEL INTRODUCTION TO GRAPH CONVOLUTIONAL NETWORKS, 18 September 2018 (2018-09-18), XP055689132, Retrieved from the Internet [retrieved on 20200406]
HAMON, RONAN ET AL.: "From graphs to signals and back: Identification of network structures using spectral analysis", ARXIV:1502.04697V3, 10 June 2016 (2016-06-10), pages 1 - 19, XP055758436, Retrieved from the Internet [retrieved on 20200406]
Attorney, Agent or Firm:
NAKAMURA Yukitaka et al. (JP)
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