2022 Fiscal Year Research-status Report
| Project/Area Number |
22K19814
|
|---|
| 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
|
| Keywords | SyncMap / Self-organization / Novel AI Paradigm / Dynamical systems |
|---|
| 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.
|
| 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.
|
| Strategy for Future Research Activity |
We will continue with the development of computer vision and classification based on SyncMap's novel learning paradigm.
|
