Maximum likelihood learning of RBMs with Gaussian visible units on the Stiefel manifold. - Ryo Karakida; Masato Okada; Shun-ichi Amari (ESANN, 2016)

Adaptive Natural Gradient Learning Algorithms for Unnormalized Statistical Models. - Ryo Karakida; Masato Okada; Shun-ichi Amari (ICANN (1), 2016)

Dynamical analysis of contrastive divergence learning: Restricted Boltzmann machines with Gaussian visible units. - Ryo Karakida; Masato Okada; Shun-ichi Amari (Neural Networks, 2016)

Information Geometry of Wasserstein Divergence. - Ryo Karakida; Shun-ichi Amari (GSI, 2017)

Information Geometry Connecting Wasserstein Distance and Kullback-Leibler Divergence via the Entropy-Relaxed Transportation Problem. - Shun-ichi Amari; Ryo Karakida; Masafumi Oizumi (CoRR, 2017)

Concept Formation and Dynamics of Repeated Inference in Deep Generative Models. - Yoshihiro Nagano; Ryo Karakida; Masato Okada (CoRR, 2017)

Statistical Neurodynamics of Deep Networks: Geometry of Signal Spaces. - Shun-ichi Amari; Ryo Karakida; Masafumi Oizumi (CoRR, 2018)

Dynamics of Learning in MLP: Natural Gradient and Singularity Revisited. - Shun-ichi Amari; Tomoko Ozeki; Ryo Karakida; Yuki Yoshida; Masato Okada (Neural Comput., 2018)

Fisher Information and Natural Gradient Learning in Random Deep Networks. - Shun-ichi Amari; Ryo Karakida; Masafumi Oizumi (AISTATS, 2019)

Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach. - Ryo Karakida; Shotaro Akaho; Shun-ichi Amari (AISTATS, 2019)

The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks. - Ryo Karakida; Shotaro Akaho; Shun-ichi Amari (NeurIPS, 2019)

Information Geometry for Regularized Optimal Transport and Barycenters of Patterns. - Shun-ichi Amari; Ryo Karakida; Masafumi Oizumi; Marco Cuturi (Neural Comput., 2019)

Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks. - Ryo Karakida; Kazuki Osawa (NeurIPS, 2020)

The Spectrum of Fisher Information of Deep Networks Achieving Dynamical Isometry. - Tomohiro Hayase; Ryo Karakida (AISTATS, 2021)

Learning Curves for Sequential Training of Neural Networks: Self-Knowledge Transfer and Forgetting. - Ryo Karakida; Shotaro Akaho (CoRR, 2021)

Self-paced data augmentation for training neural networks. - Tomoumi Takase; Ryo Karakida; Hideki Asoh (Neurocomputing, 2021)

Pathological Spectra of the Fisher Information Metric and Its Variants in Deep Neural Networks. - Ryo Karakida; Shotaro Akaho; Shun-ichi Amari (Neural Comput., 2021)

Learning curves for continual learning in neural networks: Self-knowledge transfer and forgetting. - Ryo Karakida; Shotaro Akaho (ICLR, 2022)

Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias. - Ryo Karakida; Tomoumi Takase; Tomohiro Hayase; Kazuki Osawa (ICML, 2023)

MLP-Mixer as a Wide and Sparse MLP. - Tomohiro Hayase; Ryo Karakida (CoRR, 2023)

Deep learning in random neural fields: Numerical experiments via neural tangent kernel. - Kaito Watanabe; Kotaro Sakamoto; Ryo Karakida; Sho Sonoda; Shun-ichi Amari (Neural Networks, 2023)

Attention in a Family of Boltzmann Machines Emerging From Modern Hopfield Networks. - Toshihiro Ota; Ryo Karakida (Neural Comput., 2023)

Deep Generative Replay-based Class-incremental Continual Learning in sEMG-based Pattern Recognition. - Suguru Kanoga; Ryo Karakida; Takayuki Hoshino; Yuto Okawa; Mitsunori Tada (EMBC, 2024)

On the Parameterization of Second-Order Optimization Effective towards the Infinite Width. - Satoki Ishikawa; Ryo Karakida (ICLR, 2024)

Self-attention Networks Localize When QK-eigenspectrum Concentrates. - Han Bao 002; Ryuichiro Hataya; Ryo Karakida (ICML, 2024)

Understanding MLP-Mixer as a wide and sparse MLP. - Tomohiro Hayase; Ryo Karakida (ICML, 2024)

Hierarchical Associative Memory, Parallelized MLP-Mixer, and Symmetry Breaking. - Ryo Karakida; Toshihiro Ota; Masato Taki (CoRR, 2024)

Local Loss Optimization in the Infinite Width: Stable Parameterization of Predictive Coding Networks and Target Propagation. - Satoki Ishikawa; Rio Yokota; Ryo Karakida (ICLR, 2025)

Continual learning in inertial measurement unit-based human activity recognition with user-centric class-incremental learning scenario. - Suguru Kanoga; Yuya Tsukiji; Ryo Karakida (Expert Syst. Appl., 2025)

Optimal layer selection for latent data augmentation. - Tomoumi Takase; Ryo Karakida (Neural Networks, 2025)

Recurrent Self-Attention Dynamics: An Energy-Agnostic Perspective from Jacobians. - Akiyoshi Tomihari; Ryo Karakida (CoRR, 2025)

Infinite-Width Limit of a Single Attention Layer: Analysis via Tensor Programs. - Mana Sakai; Ryo Karakida; Masaaki Imaizumi (CoRR, 2025)