2 citations · 4 across the 5 of their papers we have counts for
6 papers
Singularities of Non-negative Matrix Factorization and their application to Bayesian inference
Naoki Hayashi, Yota Maeda, Yasushi Esaki
Non-negative matrix factorization (NMF) is a singular statistical model whose Bayesian asymptotics are governed by the real log canonical threshold (RLCT). We study the local geome…
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…
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…
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…
Theoretical Analysis of the Advantage of Deepening Neural Networks
Yasushi Esaki, Yuta Nakahara, Toshiyasu Matsushima
We propose two new criteria to understand the advantage of deepening neural networks. It is important to know the expressivity of functions computable by deep neural networks in or…