26 citations · 36 across the 7 of their papers we have counts for
7 papers
Regression with Sensor Data Containing Incomplete Observations
Takayuki Katsuki, Takayuki Osogami
This paper addresses a regression problem in which output label values are the results of sensing the magnitude of a phenomenon. A low value of such labels can mean either that the…
Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction
Takayuki Katsuki, Kohei Miyaguchi, Akira Koseki +3
We address the problem of predicting when a disease will develop, i.e., medical event time (MET), from a patient's electronic health record (EHR). The MET of non-communicable disea…
Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data
Takayuki Katsuki, Kun Zhao, Takayuki Yoshizumi
This paper addresses the learning task of estimating driver drowsiness from the signals of car acceleration sensors. Since even drivers themselves cannot perceive their own drowsin…
Time-Discounting Convolution for Event Sequences with Ambiguous Timestamps
Takayuki Katsuki, Takayuki Osogami, Akira Koseki +4
This paper proposes a method for modeling event sequences with ambiguous timestamps, a time-discounting convolution. Unlike in ordinary time series, time intervals are not constant…
Consistent Nonparametric Different-Feature Selection via the Sparsest -Subgraph Problem
Satoshi Hara, Takayuki Katsuki, Hiroki Yanagisawa +4
Two-sample feature selection is the problem of finding features that describe a difference between two probability distributions, which is a ubiquitous problem in both scientific a…
Posterior Mean Super-Resolution with a Compound Gaussian Markov Random Field Prior
Takayuki Katsuki, Masato Inoue
This manuscript proposes a posterior mean (PM) super-resolution (SR) method with a compound Gaussian Markov random field (MRF) prior. SR is a technique to estimate a spatially high…
Posterior Mean Super-resolution with a Causal Gaussian Markov Random Field Prior
Takayuki Katsuki, Akira Torii, Masato Inoue
We propose a Bayesian image super-resolution (SR) method with a causal Gaussian Markov random field (MRF) prior. SR is a technique to estimate a spatially high-resolution image fro…