Universal statistics of Fisher information in deep neural networks: mean field approach R Karakida, S Akaho, S Amari International Conference on Artificial Intelligence and Statistics (AISTATS …, 2018 | 80 | 2018 |

Information geometry connecting Wasserstein distance and Kullback–Leibler divergence via the entropy-relaxed transportation problem S Amari, R Karakida, M Oizumi Information Geometry 1 (1), 13-37, 2018 | 58 | 2018 |

Dynamical analysis of contrastive divergence learning: Restricted Boltzmann machines with Gaussian visible units R Karakida, M Okada, S Amari Neural Networks 79, 78-87, 2016 | 38 | 2016 |

Fisher information and natural gradient learning in random deep networks S Amari, R Karakida, M Oizumi International Conference on Artificial Intelligence and Statistics, 694-702, 2019 | 25 | 2019 |

The normalization method for alleviating pathological sharpness in wide neural networks R Karakida, S Akaho, S Amari Advances in Neural Information Processing Systems, 6406--6416, 2019 | 23 | 2019 |

Dynamics of learning in MLP: Natural gradient and singularity revisited S Amari, T Ozeki, R Karakida, Y Yoshida, M Okada Neural computation 30 (1), 1-33, 2017 | 23 | 2017 |

Pathological Spectra of the Fisher Information Metric and Its Variants in Deep Neural Networks R Karakida, S Akaho, S Amari Neural Computation 33 (8), 2274-2307, 2021 | 19 | 2021 |

Information geometry for regularized optimal transport and barycenters of patterns S Amari, R Karakida, M Oizumi, M Cuturi Neural computation 31 (5), 827-848, 2019 | 18 | 2019 |

Statistical mechanical analysis of online learning with weight normalization in single layer perceptron Y Yoshida, R Karakida, M Okada, S Amari Journal of the Physical Society of Japan 86 (4), 044002, 2017 | 15 | 2017 |

Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks R Karakida, K Osawa Advances in Neural Information Processing Systems (NeurIPS), 2020 | 11 | 2020 |

Statistical mechanical analysis of learning dynamics of two-layer perceptron with multiple output units Y Yoshida, R Karakida, M Okada, SI Amari Journal of Physics A: Mathematical and Theoretical 52 (18), 184002, 2019 | 11 | 2019 |

Statistical neurodynamics of deep networks: Geometry of signal spaces S Amari, R Karakida, M Oizumi Nonlinear Theory and Its Applications, IEICE 10 (4), 322-336, 2019 | 7 | 2019 |

Analyzing feature extraction by contrastive divergence learning in RBMs R Karakida, M Okada, S Amari Deep learning and representation learning workshop: NIPS, 2014 | 7 | 2014 |

Self-paced data augmentation for training neural networks T Takase, R Karakida, H Asoh Neurocomputing 442, 296-306, 2021 | 6 | 2021 |

Adaptive Natural Gradient Learning Algorithms for Unnormalized Statistical Models R Karakida, M Okada, S Amari Proceedings of International Conference on Artificial Neural Networks (ICANN …, 2016 | 5 | 2016 |

Information geometry of wasserstein divergence R Karakida, S Amari International Conference on Geometric Science of Information, 119-126, 2017 | 4 | 2017 |

The spectrum of Fisher information of deep networks achieving dynamical isometry T Hayase, R Karakida International Conference on Artificial Intelligence and Statistics, 334-342, 2021 | 3 | 2021 |

Statistical neurodynamics of deep networks I S Amari, R Karakida, M Oizumi Geometry of signal spaces. arXiv, 2018 | 2 | 2018 |

Maximum likelihood learning of RBMs with Gaussian visible units on the Stiefel manifold R Karakida, M Okada, S Amari Proceedings of 24th European Symposium on Artificial Neural Networks …, 2016 | 2 | 2016 |

Input response of neural network model with lognormally distributed synaptic weights Y Nagano, R Karakida, N Watanabe, A Aoyama, M Okada Journal of the Physical Society of Japan 85 (7), 074001, 2016 | 1 | 2016 |