2 citations · 3 across the 3 of their papers we have counts for
6 papers
Self-paced Data Augmentation for Training Neural Networks
Tomoumi Takase, Ryo Karakida, Hideki Asoh
Data augmentation is widely used for machine learning; however, an effective method to apply data augmentation has not been established even though it includes several factors that…
The Spectrum of Fisher Information of Deep Networks Achieving Dynamical Isometry
Tomohiro Hayase, Ryo Karakida
The Fisher information matrix (FIM) is fundamental to understanding the trainability of deep neural nets (DNN), since it describes the parameter space's local metric. We investigat…
Pathological spectra of the Fisher information metric and its variants in deep neural networks
Ryo Karakida, Shotaro Akaho, Shun-ichi Amari
The Fisher information matrix (FIM) plays an essential role in statistics and machine learning as a Riemannian metric tensor or a component of the Hessian matrix of loss functions.…
The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks
Ryo Karakida, Shotaro Akaho, Shun-ichi Amari
Normalization methods play an important role in enhancing the performance of deep learning while their theoretical understandings have been limited. To theoretically elucidate the…
Concept Formation and Dynamics of Repeated Inference in Deep Generative Models
Yoshihiro Nagano, Ryo Karakida, Masato Okada
Deep generative models are reported to be useful in broad applications including image generation. Repeated inference between data space and latent space in these models can denois…
Information Geometry Connecting Wasserstein Distance and Kullback-Leibler Divergence via the Entropy-Relaxed Transportation Problem
Shun-ichi Amari, Ryo Karakida, Masafumi Oizumi
Two geometrical structures have been extensively studied for a manifold of probability distributions. One is based on the Fisher information metric, which is invariant under revers…