1 citations · 1 across the 3 of their papers we have counts for
9 papers
CT-OT Flow: Estimating Continuous-Time Dynamics from Discrete Temporal Snapshots
Keisuke Kawano, Takuro Kutsuna, Naoki Hayashi +2
In many real-world settings--e.g., single-cell RNA sequencing, mobility sensing, and environmental monitoring--data are observed only as temporally aggregated snapshots collected o…
Generative climate downscaling enables high-resolution compound risk assessment by preserving multivariate dependencies
Takuro Kutsuna, Noriko N. Ishizaki, Norihiro Oyama +1
Physics-based climate projections using general circulation models are essential for assessing future risks, but their coarse resolution limits regional decision-making. Statistica…
A Probabilistic Formulation of Offset Noise in Diffusion Models
Takuro Kutsuna
Diffusion models have become fundamental tools for modeling data distributions in machine learning. Despite their success, these models face challenges when generating data with ex…
Residual Prior Diffusion: A Probabilistic Framework Integrating Coarse Latent Priors with Diffusion Models
Takuro Kutsuna
Diffusion models have become a central tool in deep generative modeling, but standard formulations rely on a single network and a single diffusion schedule to transform a simple pr…
An Asymptotic Equation Linking WAIC and WBIC in Singular Models
Naoki Hayashi, Takuro Kutsuna, Sawa Takamuku
In statistical learning, models are classified as regular or singular depending on whether the mapping from parameters to probability distributions is injective. Most models with h…
Exploring Variance Reduction in Importance Sampling for Efficient DNN Training
Takuro Kutsuna
Importance sampling is widely used to improve the efficiency of deep neural network (DNN) training by reducing the variance of gradient estimators. However, efficiently assessing t…