| 2025 | ICLR | Local Loss Optimization in the Infinite Width: Stable Parameterization of Predictive Coding Networks and Target Propagation. | Satoki Ishikawa, Rio Yokota, Ryo Karakida |
| 2024 | ICLR | On the Parameterization of Second-Order Optimization Effective towards the Infinite Width. | Satoki Ishikawa, Ryo Karakida |
| 2024 | ICML | Self-attention Networks Localize When QK-eigenspectrum Concentrates. | Han Bao, Ryuichiro Hataya, Ryo Karakida |
| 2024 | ICML | Understanding MLP-Mixer as a wide and sparse MLP. | Tomohiro Hayase, Ryo Karakida |
| 2023 | ICML | Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias. | Ryo Karakida, Tomoumi Takase, Tomohiro Hayase, Kazuki Osawa |
| 2022 | ICLR | Learning curves for continual learning in neural networks: Self-knowledge transfer and forgetting. | Ryo Karakida, Shotaro Akaho |
| 2021 | AISTATS | The Spectrum of Fisher Information of Deep Networks Achieving Dynamical Isometry. | Tomohiro Hayase, Ryo Karakida |
| 2019 | AISTATS | Fisher Information and Natural Gradient Learning in Random Deep Networks. | Shun-ichi Amari, Ryo Karakida, Masafumi Oizumi |
| 2019 | AISTATS | Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach. | Ryo Karakida, Shotaro Akaho, Shun-ichi Amari |
| 2016 | ESANN | Maximum likelihood learning of RBMs with Gaussian visible units on the Stiefel manifold. | Ryo Karakida, Masato Okada, Shun-ichi Amari |
| 2016 | ICANN | Adaptive Natural Gradient Learning Algorithms for Unnormalized Statistical Models. | Ryo Karakida, Masato Okada, Shun-ichi Amari |