296 citations · 312 across the 8 of their papers we have counts for
8 papers
Flow Matching-Based PET Image Reconstruction
Fumio Hashimoto, Ziqian Huang, Tatsuya Yokota +1
Generative models have shown strong potential for positron emission tomography (PET) image reconstruction. Although diffusion model-based reconstruction methods have demonstrated p…
Soft Smoothness for Audio Inpainting Using a Latent Matrix Model in Delay-embedded Space
Tatsuya Yokota
Here, we propose a new reconstruction method of smooth time-series signals. A key concept of this study is not considering the model in signal space, but in delay-embedded space. I…
Manifold Modeling in Quotient Space: Learning An Invariant Mapping with Decodability of Image Patches
Tatsuya Yokota, Hidekata Hontani
This study proposes a framework for manifold learning of image patches using the concept of equivalence classes: manifold modeling in quotient space (MMQS). In MMQS, we do not cons…
Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting
Qiquan Shi, Jiaming Yin, Jiajun Cai +5
This work proposes a novel approach for multiple time series forecasting. At first, multi-way delay embedding transform (MDT) is employed to represent time series as low-rank block…
Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image Prior
Tatsuya Yokota, Hidekata Hontani, Qibin Zhao +1
Deep image prior (DIP), which utilizes a deep convolutional network (ConvNet) structure itself as an image prior, has attracted attentions in computer vision and machine learning c…
Missing Slice Recovery for Tensors Using a Low-rank Model in Embedded Space
Tatsuya Yokota, Burak Erem, Seyhmus Guler +2
Let us consider a case where all of the elements in some continuous slices are missing in tensor data. In this case, the nuclear-norm and total variation regularization methods usu…
Simultaneous Tensor Completion and Denoising by Noise Inequality Constrained Convex Optimization
Tatsuya Yokota, Hidekata Hontani
Tensor completion is a technique of filling missing elements of the incomplete data tensors. It being actively studied based on the convex optimization scheme such as nuclear-norm…
Smooth PARAFAC Decomposition for Tensor Completion
Tatsuya Yokota, Qibin Zhao, Andrzej Cichocki
In recent years, low-rank based tensor completion, which is a higher-order extension of matrix completion, has received considerable attention. However, the low-rank assumption is…