| Budget Amount *help |
¥16,120,000 (Direct Cost: ¥12,400,000、Indirect Cost: ¥3,720,000)
Fiscal Year 2018: ¥5,460,000 (Direct Cost: ¥4,200,000、Indirect Cost: ¥1,260,000)
Fiscal Year 2017: ¥5,850,000 (Direct Cost: ¥4,500,000、Indirect Cost: ¥1,350,000)
Fiscal Year 2016: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
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| Outline of Final Research Achievements |
The objective of research is to present a generalized framework for the input of multiple matrices sharing dimensions and efficient solutions under this framework. We show two example results among our various results obtained during our research period:
1. For the input of a tensor and a matrix which share one dimension, we define a new norm and propose an efficient learning algorithm to estimate the norm. We analyze the property of the norm and empirically show the performance advantage of our norm and algorithm using both synthetic and real-world datasets. The results were summarized into a publication appeared in Neural Computation and also a paper appeared in NeurIPS, one of the top machine learning conferences.
2. We develop an efficient, scalable probabilistic-model based approach for the input of multiple matrices sharing dimensions. This result was published as a paper appeared in AAAI, one of the top artificial intelligence conferences.
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