1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Nansde-net: A neural sde framework for generating time series with memory
Hiromu Ozai, Kei Nakagawa
Modeling time series with long- or short-memory characteristics is a fundamental challenge in many scientific and engineering domains. While fractional Brownian motion has been wid…
cs.CL2024★ 1 cited
CFTM: Continuous time fractional topic model
Kei Nakagawa, Kohei Hayashi, Yugo Fujimoto
In this paper, we propose the Continuous Time Fractional Topic Model (cFTM), a new method for dynamic topic modeling. This approach incorporates fractional Brownian motion~(fBm) to…
cs.LG2020★ 1 cited
Mean-Variance Efficient Reinforcement Learning with Applications to Dynamic Financial Investment
Masahiro Kato, Kei Nakagawa, Kenshi Abe +2
This study investigates the mean-variance (MV) trade-off in reinforcement learning (RL), an instance of the sequential decision-making under uncertainty. Our objective is to obtain…