A comprehensive study of learning, inference, and inverse problems based on the spin-glass theory
Project/Area Number |
26870185
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Research Category |
Grant-in-Aid for Young Scientists (B)
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Allocation Type | Multi-year Fund |
Research Field |
Theory of informatics
Mathematical physics/Fundamental condensed matter physics
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Research Institution | Tokyo Institute of Technology |
Principal Investigator |
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Project Period (FY) |
2014-04-01 – 2017-03-31
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Project Status |
Completed (Fiscal Year 2016)
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Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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Keywords | 逆問題・多自由度推定 / 統計的学習理論・機械学習 / 統計物理学 / 最大エントロピー法 / スパースモデリング / 逆問題 / 機械学習 / ニューラルネットワーク / ベイズ統計 |
Outline of Final Research Achievements |
The purpose of this study is to clarify general and theoretical aspects of statistical learning theory and inverse problems by using the spin-glass theory from statistical physics. The objective of the discipline is to infer a correct probability distribution from a limited number of observations. Hence common important theoretical questions are as follows: Clarifying the achievable theoretic limit given the limited observations; designing algorithms to achieve the limit; applying the algorithms to real-world dataset. Our actual research processes were roughly categorized into two processes: One is to clarify the theoretical limit by utilizing the spin-glass theory; the other is to invent algorithms and apply them to neurons' firing data and natural image processing. Our study has clarified that two commonly used frameworks, the maximum entropy principle and sparse modelling, have their own theoretical limits and have contrasting pros and cons.
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Report
(4 results)
Research Products
(35 results)
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[Presentation] 過完備基底圧縮の統計力学的解析2014
Author(s)
中西(大野)義典,小渕智之,岡田真人,樺島祥介
Organizer
第37回情報理論とその応用シンポジウム
Place of Presentation
宇奈月ニューオータニホテル
Year and Date
2014-12-10
Related Report
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