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Tensor Network Representation for Machine Learning: Theoretical Study and Algorithms Development

Research Project

Project/Area Number 20H04249
Research Category

Grant-in-Aid for Scientific Research (B)

Allocation TypeSingle-year Grants
Section一般
Review Section Basic Section 61030:Intelligent informatics-related
Research InstitutionInstitute of Physical and Chemical Research

Principal Investigator

ZHAO QIBIN  国立研究開発法人理化学研究所, 革新知能統合研究センター, チームリーダー (30599618)

Co-Investigator(Kenkyū-buntansha) 曹 建庭  埼玉工業大学, 工学部, 教授 (20306989)
横田 達也  名古屋工業大学, 工学(系)研究科(研究院), 助教 (80733964)
Project Period (FY) 2020-04-01 – 2024-03-31
Project Status Granted (Fiscal Year 2021)
Budget Amount *help
¥17,550,000 (Direct Cost: ¥13,500,000、Indirect Cost: ¥4,050,000)
Fiscal Year 2022: ¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2021: ¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2020: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
KeywordsTensor Networks / Tensor networks / Machine Learning
Outline of Research at the Start

Tensor networks (TNs) have recently gained increasing attentions in machine learning, data mining and computer vision fields due to its effectiveness in efficient computation and model compression in deep learning. However, there are many open problems that are still unexplored, which limits its impact in machine learning. Therefore, our research aims to investigate the fundamental theory and develop scalable and efficient learning algorithms for TN. Moreover, we will further explore what challenging problems in machine learning can be solved by TN technology.

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Published: 2020-04-28   Modified: 2022-04-19  

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