7 accepted papers
This study tackles the problem of generating electrocardiograms (ECG) from photoplethysmograms (PPG) data collected by wearable devices such as smartwatches. Existing methods based on diffusion models successfully generate high-quality ECGs but suffer from slow inference since diffusion models itera…
Shuffled regression is the problem of learning regression functions from shuffled data where the correspondence between the input features and target response is unknown. This paper proposes a probabilistic model for shuffled regression called Gaussian Process Shuffled Regression (GPSR). By introduc…
Dynamic mode decomposition (DMD) has been applied for analyzing various nonlinear dynamic systems. In this study, we clarify the relationship among the variants of DMD and nonnegative matrix factorization (NMF), which is also widely used for audio source separation and document clustering. We show t…
Shuffled regression is the problem of learning regression models from shuffled data that consists of a set of input features and a set of target outputs where the correspondence between the input and output is unknown. This study proposes a new deep learning method for shuffled regression called Shu…
The research field of stochastic matching has yielded many developments for various applications. In most stochastic matching problems, the probability distributions inherent in the nodes and edges are set a priori, and are not controllable. However, many matching services have options, which we cal…
Due to the difficulty of comprehensive data collection, created by factors such as privacy protection and sensor device limitations, we often need to analyze incomplete transition data where some information is missing from the ideal (complete) transition data. In this paper, we propose a new method…
In this paper, we propose a probabilistic model for analyzing the generalized interval valued matrix, a matrix that has scalar valued elements and bounded/unbounded interval valued elements. We derive a majorization minimization algorithm for parameter estimation and prove that the objective functio…