25 citations · 29 across the 4 of their papers we have counts for
6 papers
An Empirical Study of the Effects of Sample-Mixing Methods for Efficient Training of Generative Adversarial Networks
Makoto Takamoto, Yusuke Morishita
It is well-known that training of generative adversarial networks (GANs) requires huge iterations before the generator's providing good-quality samples. Although there are several…
An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation
Makoto Takamoto, Yusuke Morishita, Hitoshi Imaoka
Compressing deep neural network (DNN) models becomes a very important and necessary technique for real-world applications, such as deploying those models on mobile devices. Knowled…
Evolution of three-dimensional Relativistic Ion Weibel Instability: Competition with Kink Instability
Makoto Takamoto, Yosuke Matsumoto, Tsunehiko N. Kato
In this paper, we report our recent findings on the relativistic Weibel instability and its nonlinear saturation by performing numerical simulations of collisionless plasmas. Analy…
Magnetic Field Saturation of the Ion Weibel Instability in Interpenetrating Relativistic Plasmas
Makoto Takamoto, Yosuke Matsumoto, Tsunehiko N. Kato
The time evolution and saturation of the Weibel instability at the ion Alfvén current are presented by ab initio particle-in-cell simulations. We found that the ion Weibel current…
Evolution of 3-dimensional Relativistic Current Sheets and Development of Self-Generated Turbulence
Makoto Takamoto
In this paper, the temporal evolution of 3-dimensional relativistic current sheets in Poynting-dominated plasma is studied for the first time. Over the past few decades, a lot of e…
Strong Coupling of Alfvén and Fast Modes in Compressible Relativistic Magnetohydrodynamic Turbulence in Magnetically-Dominated Plasmas
Makoto Takamoto, Alexandre Lazarian
In this paper, we report our detailed analysis of the new strong-coupling regime between Alfvén and fast modes in Poynting-dominated plasma turbulence, reported in our previous wor…