39 citations · 84 across the 9 of their papers we have counts for
9 papers
On a Proof of the Convergence Speed of a Second-order Recurrence Formula in the Arimoto-Blahut Algorithm
Kenji Nakagawa, Yoshinori Takei, Shin-ichiro Hara
In [8] (Nakagawa, et.al., IEEE Trans. IT, 2021), we investigated the convergence speed of the Arimoto-Blahut algorithm. In [8], the convergence of the order was analyzed b…
GraphTune: A Learning-based Graph Generative Model with Tunable Structural Features
Kohei Watabe, Shohei Nakazawa, Yoshiki Sato +2
Generative models for graphs have been actively studied for decades, and they have a wide range of applications. Recently, learning-based graph generation that reproduces real-worl…
A Tunable Model for Graph Generation Using LSTM and Conditional VAE
Shohei Nakazawa, Yoshiki Sato, Kenji Nakagawa +2
With the development of graph applications, generative models for graphs have been more crucial. Classically, stochastic models that generate graphs with a pre-defined probability…
Analysis of the Convergence Speed of the Arimoto-Blahut Algorithm by the Second Order Recurrence Formula
Kenji Nakagawa, Yoshinori Takei, Shin-ichiro Hara +1
In this paper, we investigate the convergence speed of the Arimoto-Blahut algorithm. For many channel matrices the convergence is exponential, but for some channel matrices it is s…
Analysis for the Slow Convergence in Arimoto Algorithm
Kenji Nakagawa, Yoshinori Takei, Kohei Watabe
In this paper, we investigate the convergence speed of the Arimoto algorithm. By analyzing the Taylor expansion of the defining function of the Arimoto algorithm, we will clarify t…
On the Search Algorithm for the Output Distribution that Achieves the Channel Capacity
Kenji Nakagawa, Kohei Watabe, Takuto Sabu
We consider a search algorithm for the output distribution that achieves the channel capacity of a discrete memoryless channel. We will propose an algorithm by iterated projections…