Project/Area Number |
22K19814
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Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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Allocation Type | Multi-year Fund |
Review Section |
Medium-sized Section 61:Human informatics and related fields
|
Research Institution | Kyushu University |
Principal Investigator |
VARGAS DANILO 九州大学, システム情報科学研究院, 准教授 (00795536)
|
Project Period (FY) |
2022-06-30 – 2024-03-31
|
Project Status |
Granted (Fiscal Year 2022)
|
Budget Amount *help |
¥6,500,000 (Direct Cost: ¥5,000,000、Indirect Cost: ¥1,500,000)
Fiscal Year 2023: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2022: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
|
Keywords | SyncMap / Self-organization / Novel AI Paradigm / Dynamical systems / 自己組織化 / Dynamical Equations / New Learning Paradigm |
Outline of Research at the Start |
[Vargas, Asabuki AAAI21]に公開されたSyncMapは(a 開拓な学習手法)従来と異なる学習システムであり、最適化によらなく、力学系のみに基づいておる。(b 高い精度)ほとんどのタスクで深層学習を含めてすべてのアルゴリズムを超え、非常に正確であった。さらに、(c 高い適応度)常に自己更新する動的方程式に基づいているため、適応は固有の機能であり、したがって、定期的に変化する問題は、より簡単に解決できると紹介した。では、SyncMapは上記の三点によって有望を持つ学習システムと考えられる。SyncMapという新たな学習パラダイムに基づいて、新しい人工知能の基礎を開拓する研究である。
|
Outline of Annual Research Achievements |
In this research we further developed the novel machine learning paradigm based solely on dynamical equations and no optimization. SyncMap was further improved in stability (Symmetrical SyncMap), to deal with high dimensionality (Magnum) and also to work with changing hierarchies (Sigma). Symmetrical SyncMap improves the equations of SyncMap to deal with long-term stability of the initial equations. Magnum uses multiple Inertia-SyncMaps in subsets of variables, enabling it to deal with high dimensional problems. Sigma achieves the astonishing feat of both being accurate enough to predict the structure of hierarchical input structures at the same time as being adaptive enough to deal with changes in them throughout the experiments. All these methods were extensively tested and their results were submitted to top journals in the area. Hopefully, most of them will become available in the next few months. We also developed two key understanding of how input sequences can be used generally to deal with images and other non-sequential input. Based on these new findings, we have proceeded to develop a novel architecture for computer vision and two methodologies for supervised learning classification tasks.
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Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
Currently, with three improvements of SyncMap finalized and submitted to journals, we have already proceeded to the last stages of development of a new (a) computer vision architecture and (b) classification methodology. We expect to finalize the experiments of both and submit them within this year.
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Strategy for Future Research Activity |
We will continue with the development of computer vision and classification based on SyncMap's novel learning paradigm.
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Report
(1 results)
Research Products
(7 results)