98 citations
- Waseda UniversityJP10 papers
- The University of TokyoJP9 papers
- Shibaura Institute of TechnologyJP8 papers
- Kyushu UniversityJP6 papers
- Japan Atomic Energy AgencyJP5 papers
- Advanced Science Research CenterJP3 papers
- Kagoshima UniversityJP3 papers
- National Institute for Materials ScienceJP3 papers
- Oak Ridge National LaboratoryUS3 papers
- Tokyo University of ScienceJP3 papers
- Université Paris-SudFR3 papers
- Ajou UniversityKR2 papers
136 papers
Privacy-Preserving Synthetic Dataset of Individual Daily Trajectories for City-Scale Mobility Analytics
Jun'ichi Ozaki, Ryosuke Susuta, Takuhiro Moriyama +1
Urban mobility data are indispensable for urban planning, transportation demand forecasting, pandemic modeling, and many other applications; however, individual mobile phone-derive…
Estimation of time-varying treatment effects using marginal structural models dependent on partial treatment history
Nodoka Seya, Masataka Taguri, Takeo Ishii
Inverse probability (IP) weighting of marginal structural models (MSMs) can provide consistent estimators of time-varying treatment effects under correct model specifications and i…
Data-driven study of the enthalpy of mixing in the liquid phase
Guillaume Deffrennes, Bengt Hallstedt, Taichi Abe +5
The enthalpy of mixing in the liquid phase is a thermodynamic property reflecting interactions between elements that is key to predict phase transformations. Widely used models exi…
A machine learning-based classification approach for phase diagram prediction
Guillaume Deffrennes, Kei Terayama, Taichi Abe +1
Knowledge of phase diagrams is essential for material design as it helps in understanding microstructure evolution during processing. The determination of phase diagrams is thus on…
Bayesian optimization package: PHYSBO
Yuichi Motoyama, Ryo Tamura, Kazuyoshi Yoshimi +3
PHYSBO (optimization tools for PHYSics based on Bayesian Optimization) is a Python library for fast and scalable Bayesian optimization. It has been developed mainly for application…
Xi-Vector Embedding for Speaker Recognition
Kong Aik Lee, Qiongqiong Wang, Takafumi Koshinaka
We present a Bayesian formulation for deep speaker embedding, wherein the xi-vector is the Bayesian counterpart of the x-vector, taking into account the uncertainty estimate. On th…