Search Results for author: Naotake Natori

Found 2 papers, 0 papers with code

Absorbing Phase Transitions in Artificial Deep Neural Networks

no code implementations5 Jul 2023 Keiichi Tamai, Tsuyoshi Okubo, Truong Vinh Truong Duy, Naotake Natori, Synge Todo

More specifically, we study the order-to-chaos transition in the fully-connected feedforward neural networks and the convolutional ones to show that (i) there is a well-defined transition from the ordered state to the chaotics state even for the finite networks, and (ii) difference in architecture is reflected in that of the universality class of the transition.

Rethinking the role of normalization and residual blocks for spiking neural networks

no code implementations3 Mar 2022 Shin-ichi Ikegawa, Ryuji Saiin, Yoshihide Sawada, Naotake Natori

Biologically inspired spiking neural networks (SNNs) are widely used to realize ultralow-power energy consumption.

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