| 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 | 138 | 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, 13-37, 2018 | 77 | 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 | 55 | 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 | 42 | 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 | 41 | 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 | 35 | 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 | 33 | 2021 |
| 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 | 26 | 2020 |
| 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 | 21 | 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 | 17 | 2017 |
| Self-paced data augmentation for training neural networks T Takase, R Karakida, H Asoh Neurocomputing 442, 296-306, 2021 | 16 | 2021 |
| Learning curves for continual learning in neural networks: Self-knowledge transfer and forgetting R Karakida, S Akaho International Conference on Learning Representations, 2022 | 12 | 2022 |
| 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 | 10 | 2019 |
| Understanding gradient regularization in deep learning: Efficient finite-difference computation and implicit bias R Karakida, T Takase, T Hayase, K Osawa International Conference on Machine Learning, 15809-15827, 2023 | 8 | 2023 |
| 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 | 7 | 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 | 7 | 2016 |
| 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 |
| Information geometry of wasserstein divergence R Karakida, S Amari International Conference on Geometric Science of Information, 119-126, 2017 | 5 | 2017 |
| 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 | 3 | 2016 |
Amari S*在 brain.riken.jp 的电子邮件经过验证
