3 citations · 8 across the 13 of their papers we have counts for
13 papers
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…
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…
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…
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…
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…
Linearly Constrained Weights: Reducing Activation Shift for Faster Training of Neural Networks
Takuro Kutsuna
In this paper, we first identify activation shift, a simple but remarkable phenomenon in a neural network in which the preactivation value of a neuron has non-zero mean that depend…
One-Shot Domain Incremental Learning
Yasushi Esaki, Satoshi Koide, Takuro Kutsuna
Domain incremental learning (DIL) has been discussed in previous studies on deep neural network models for classification. In DIL, we assume that samples on new domains are observe…
Accuracy-Preserving Calibration via Statistical Modeling on Probability Simplex
Yasushi Esaki, Akihiro Nakamura, Keisuke Kawano +2
Classification models based on deep neural networks (DNNs) must be calibrated to measure the reliability of predictions. Some recent calibration methods have employed a probabilist…
Minimal Sufficient Views: A DNN model making predictions with more evidence has higher accuracy
Keisuke Kawano, Takuro Kutsuna, Keisuke Sano
Deep neural networks (DNNs) exhibit high performance in image recognition; however, the reasons for their strong generalization abilities remain unclear. A plausible hypothesis is…
Supervised Contrastive Learning with Heterogeneous Similarity for Distribution Shifts
Takuro Kutsuna
Distribution shifts are problems where the distribution of data changes between training and testing, which can significantly degrade the performance of a model deployed in the rea…
StyleDiff: Attribute Comparison Between Unlabeled Datasets in Latent Disentangled Space
Keisuke Kawano, Takuro Kutsuna, Ryoko Tokuhisa +2
One major challenge in machine learning applications is coping with mismatches between the datasets used in the development and those obtained in real-world applications. These mis…
Neural Time Warping For Multiple Sequence Alignment
Keisuke Kawano, Takuro Kutsuna, Satoshi Koide
Multiple sequences alignment (MSA) is a traditional and challenging task for time-series analyses. The MSA problem is formulated as a discrete optimization problem and is typically…