16 citations · 25 across the 4 of their papers we have counts for
4 papers
Learning to imitate stochastic time series in a compositional way by chaos
Jun Namikawa, Jun Tani
This study shows that a mixture of RNN experts model can acquire the ability to generate sequences combining multiple primitive patterns by means of self-organizing chaos. By train…
A model for learning to segment temporal sequences, utilizing a mixture of RNN experts together with adaptive variance
Jun Namikawa, Jun Tani
This paper proposes a novel learning method for a mixture of recurrent neural network (RNN) experts model, which can acquire the ability to generate desired sequences by dynamicall…
Chaotic itinerancy and power-law residence time distribution in stochastic dynamical system
Jun Namikawa
To study a chaotic itinerant motion among varieties of ordered states, we propose a stochastic model based on the mechanism of chaotic itinerancy. The model consists of a random wa…
Dynamics and computation in functional shifts
Jun Namikawa, Takashi Hashimoto
We introduce a new type of shift dynamics as an extended model of symbolic dynamics, and investigate the characteristics of shift spaces from the viewpoints of both dynamics and co…