Dynamic Neural Architecture Warping for Time Series Recognition

2022 Fiscal Year Final Research Report

• Project/Area Number: 21K17808
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 61030:Intelligent informatics-related
• Research Institution: Kyushu University
• Principal Investigator: IWANA BRIAN KENJI 九州大学, システム情報科学研究院, 准教授 (90852723)
• Project Period (FY): 2021-04-01 – 2023-03-31
• Keywords: Time Series / Neural Network / Dynamic Programming / Pattern Recognition / Machine Learning

Outline of Final Research Achievements

The research from this grant had wide successes in time series recognition, neural networks, and pattern recognition. Specifically, research wad done on dynamically warping the representations and the architectures of neural networks and applying it to new applications.

For research results, there were six peer-reviewed international journal publications, nine peer-reviewed international conference publications, and four Japanese conference publications. There are also three papers currently under review and two in the process of writing. All of these publications are high level journals and conferences.

Free Research Field

Pattern Recognition

Academic Significance and Societal Importance of the Research Achievements

この研究は、ニューラルネットワークの頑健性と適用範囲の向上において重要です。多くの出版物は、時系列認識や文書認識の研究において長期的な影響を持ちます。