Ryo Karakida

According to our database, Ryo Karakida authored at least 29 papers between 2016 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

2025

Optimal layer selection for latent data augmentation.

Tomoumi Takase, Ryo Karakida

Neural Networks, 2025

2024

Local Loss Optimization in the Infinite Width: Stable Parameterization of Predictive Coding Networks and Target Propagation.

Satoki Ishikawa, Rio Yokota, Ryo Karakida

CoRR, 2024

Hierarchical Associative Memory, Parallelized MLP-Mixer, and Symmetry Breaking.

Ryo Karakida, Toshihiro Ota, Masato Taki

CoRR, 2024

Understanding MLP-Mixer as a wide and sparse MLP.

Tomohiro Hayase, Ryo Karakida

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Self-attention Networks Localize When QK-eigenspectrum Concentrates.

Han Bao, Ryuichiro Hataya, Ryo Karakida

Proceedings of the Forty-first International Conference on Machine Learning, 2024

On the Parameterization of Second-Order Optimization Effective towards the Infinite Width.

Satoki Ishikawa, Ryo Karakida

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Deep Generative Replay-based Class-incremental Continual Learning in sEMG-based Pattern Recognition.

Suguru Kanoga, Ryo Karakida, Takayuki Hoshino, Yuto Okawa, Mitsunori Tada

Proceedings of the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2024

2023

Attention in a Family of Boltzmann Machines Emerging From Modern Hopfield Networks.

Toshihiro Ota, Ryo Karakida

Neural Comput., August, 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, March, 2023

MLP-Mixer as a Wide and Sparse MLP.

Tomohiro Hayase, Ryo Karakida

CoRR, 2023

Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias.

Ryo Karakida, Tomoumi Takase, Tomohiro Hayase, Kazuki Osawa

Proceedings of the International Conference on Machine Learning, 2023

2022

Learning curves for continual learning in neural networks: Self-knowledge transfer and forgetting.

Ryo Karakida, Shotaro Akaho

Proceedings of the Tenth International Conference on Learning Representations, 2022

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

Self-paced data augmentation for training neural networks.

Tomoumi Takase, Ryo Karakida, Hideki Asoh

Neurocomputing, 2021

Learning Curves for Sequential Training of Neural Networks: Self-Knowledge Transfer and Forgetting.

Ryo Karakida, Shotaro Akaho

CoRR, 2021

The Spectrum of Fisher Information of Deep Networks Achieving Dynamical Isometry.

Tomohiro Hayase, Ryo Karakida

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks.

Ryo Karakida, Kazuki Osawa

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Information Geometry for Regularized Optimal Transport and Barycenters of Patterns.

Shun-ichi Amari, Ryo Karakida, Masafumi Oizumi, Marco Cuturi

Neural Comput., 2019

The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks.

Ryo Karakida, Shotaro Akaho, Shun-ichi Amari

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach.

Ryo Karakida, Shotaro Akaho, Shun-ichi Amari

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Fisher Information and Natural Gradient Learning in Random Deep Networks.

Shun-ichi Amari, Ryo Karakida, Masafumi Oizumi

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

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

Statistical Neurodynamics of Deep Networks: Geometry of Signal Spaces.

Shun-ichi Amari, Ryo Karakida, Masafumi Oizumi

CoRR, 2018

2017

Concept Formation and Dynamics of Repeated Inference in Deep Generative Models.

Yoshihiro Nagano, Ryo Karakida, Masato Okada

CoRR, 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

Information Geometry of Wasserstein Divergence.

Ryo Karakida, Shun-ichi Amari

Proceedings of the Geometric Science of Information - Third International Conference, 2017

2016

Dynamical analysis of contrastive divergence learning: Restricted Boltzmann machines with Gaussian visible units.

Ryo Karakida, Masato Okada, Shun-ichi Amari

Neural Networks, 2016

Adaptive Natural Gradient Learning Algorithms for Unnormalized Statistical Models.

Ryo Karakida, Masato Okada, Shun-ichi Amari

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2016, 2016

Maximum likelihood learning of RBMs with Gaussian visible units on the Stiefel manifold.

Ryo Karakida, Masato Okada, Shun-ichi Amari

Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016