3 papers
cs.LG2024
Two stages domain invariant representation learners solve the large co-variate shift in unsupervised domain adaptation with two dimensional data domains
Hisashi Oshima, Tsuyoshi Ishizone, Tomoyuki Higuchi
Recent developments in the unsupervised domain adaptation (UDA) enable the unsupervised machine learning (ML) prediction for target data, thus this will accelerate real world appli…
stat.ML2020
Ensemble Kalman Variational Objectives: Nonlinear Latent Trajectory Inference with A Hybrid of Variational Inference and Ensemble Kalman Filter
Tsuyoshi Ishizone, Tomoyuki Higuchi, Kazuyuki Nakamura
Variational inference (VI) combined with Bayesian nonlinear filtering produces state-of-the-art results for latent time-series modeling. A body of recent work has focused on sequen…
stat.CO2020
Real-time Linear Operator Construction and State Estimation with the Kalman Filter
Tsuyoshi Ishizone, Kazuyuki Nakamura
The Kalman filter is the most powerful tool for estimation of the states of a linear Gaussian system. In addition, using this method, an expectation maximization algorithm can be u…