Shun-ichi Amari

Affiliations: 
RIKEN 
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"Shun-ichi Amari"
Mean distance: 14.21 (cluster 17)
 

Children

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HaDi MaBouDi grad student IPM
Taro Toyoizumi grad student RIKEN BSI
Wakako Nakamura post-doc RIKEN
Masafumi Oizumi post-doc RIKEN
Si Wu post-doc Brain Science Institute, RIKEN
Masami Tatsuno post-doc 1999-2002 RIKEN BSI
Kosuke Hamaguchi post-doc 2004-2008 RIKEN
Keiji Miura research scientist
Shigeru Shinomoto research scientist
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Publications

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Ito S, Oizumi M, Amari S. (2020) Unified framework for the entropy production and the stochastic interaction based on information geometry Physical Review Research. 2
Amari SI, Karakida R, Oizumi M, et al. (2019) Information Geometry for Regularized Optimal Transport and Barycenters of Patterns. Neural Computation. 1-22
Amari S, Karakida R, Oizumi M. (2019) Statistical neurodynamics of deep networks: geometry of signal spaces Nonlinear Theory and Its Applications, Ieice. 10: 322-336
Yoshida Y, Karakida R, Okada M, et al. (2019) Statistical mechanical analysis of learning dynamics of two-layer perceptron with multiple output units Journal of Physics a: Mathematical and Theoretical. 52: 184002
Kass RE, Amari SI, Arai K, et al. (2018) Computational Neuroscience: Mathematical and Statistical Perspectives. Annual Review of Statistics and Its Application. 5: 183-214
Amari S, Karakida R, Oizumi M. (2018) Information geometry connecting Wasserstein distance and Kullback–Leibler divergence via the entropy-relaxed transportation problem Information Geometry. 1: 13-37
Amari SI, Ozeki T, Karakida R, et al. (2017) Dynamics of Learning in MLP: Natural Gradient and Singularity Revisited. Neural Computation. 1-33
Yoshida Y, Karakida R, Okada M, et al. (2017) Statistical Mechanical Analysis of Online Learning with Weight Normalization in Single Layer Perceptron Journal of the Physical Society of Japan. 86: 044002
Oizumi M, Tsuchiya N, Amari SI. (2016) Unified framework for information integration based on information geometry. Proceedings of the National Academy of Sciences of the United States of America
Karakida R, Okada M, Amari SI. (2016) Dynamical analysis of contrastive divergence learning: Restricted Boltzmann machines with Gaussian visible units. Neural Networks : the Official Journal of the International Neural Network Society. 79: 78-87
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