5 papers
Bayesian Inference of Discretization Error Means in ODEs via Ensemble Kalman Filtering
Shoji Toyota, Yuto Miyatake
We propose a Bayesian framework to quantify discretization errors in numerical solutions of ODE models based on observational data. The discretization error is modeled as a random…
Handwriting Trajectory Recovery with Diffusion Models
Hiroki Nagamatsu, Shoji Toyota, Seiichi Uchida
Recovering online pen trajectories from offline handwriting images, often referred to as handwriting trajectory recovery (stroke recovery), is an offline-to-online conversion task…
Self-Organizing Score-based Data Assimilation
Yuma Yamaoka, Seiichi Uchida, Shoji Toyota
A state-space model is a statistical framework for inferring latent states from observed time-series data. However, inference with nonlinear and high-dimensional state-space models…
Compositional simulation-based inference for time series
Manuel Gloeckler, Shoji Toyota, Kenji Fukumizu +1
Amortized simulation-based inference (SBI) methods train neural networks on simulated data to perform Bayesian inference. While this strategy avoids the need for tractable likeliho…
Out-of-Distribution Optimality of Invariant Risk Minimization
Shoji Toyota, Kenji Fukumizu
Deep Neural Networks often inherit spurious correlations embedded in training data and hence may fail to generalize to unseen domains, which have different distributions from the d…