2 citations · 9 across the 18 of their papers we have counts for
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cs.LG2020★ 1 cited
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…
stat.ML2020
Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks
Ryo Karakida, Kazuki Osawa
Natural Gradient Descent (NGD) helps to accelerate the convergence of gradient descent dynamics, but it requires approximations in large-scale deep neural networks because of its h…
stat.ML2020
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…