Zobrazeno 1 - 10
of 188
pro vyhledávání: '"Yukawa, Masahiro"'
Autor:
Yukawa, Masahiro, Yamada, Isao
This paper addresses explainability of the operator-regularization approach under the use of monotone Lipschitz-gradient (MoL-Grad) denoiser -- an operator that can be expressed as the Lipschitz continuous gradient of a differentiable convex function
Externí odkaz:
http://arxiv.org/abs/2406.04676
This paper presents a decentralized algorithm for solving distributed convex optimization problems in dynamic networks with time-varying objectives. The unique feature of the algorithm lies in its ability to accommodate a wide range of communication
Externí odkaz:
http://arxiv.org/abs/2307.04913
Publikováno v:
IEEE Transactions on Signal Processing, 2023
We present an efficient mathematical framework based on the linearly-involved Moreau-enhanced-over-subspace (LiMES) model. Two concrete applications are considered: sparse modeling and robust regression. The popular minimax concave (MC) penalty for s
Externí odkaz:
http://arxiv.org/abs/2201.03235
Publikováno v:
In Signal Processing December 2024 225
This paper presents a convex-analytic framework to learn sparse graphs from data. While our problem formulation is inspired by an extension of the graphical lasso using the so-called combinatorial graph Laplacian framework, a key difference is the us
Externí odkaz:
http://arxiv.org/abs/2109.08666
The relaxed zero-forcing (RZF) beamformer is a quadratically-and-linearly constrained minimum variance beamformer. The central question addressed in this paper is whether RZF performs better than the widely-used minimum variance distortionless respon
Externí odkaz:
http://arxiv.org/abs/2109.05342
Autor:
Kaneko, Yuhei, Muramatsu, Shogo, Yasuda, Hiroyasu, Hayasaka, Kiyoshi, Otake, Yu, Ono, Shunsuke, Yukawa, Masahiro
This work proposes convolutional-sparse-coded dynamic mode decomposition (CSC-DMD) by unifying extended dynamic mode decomposition (EDMD) and convolutional sparse coding. EDMD is a data driven analysis method for describing a nonlinear dynamical syst
Externí odkaz:
http://arxiv.org/abs/1811.07281
Motivated by the success of reinforcement learning (RL) for discrete-time tasks such as AlphaGo and Atari games, there has been a recent surge of interest in using RL for continuous-time control of physical systems (cf. many challenging tasks in Open
Externí odkaz:
http://arxiv.org/abs/1806.02985
We propose an adaptive scheme for distributed learning of nonlinear functions by a network of nodes. The proposed algorithm consists of a local adaptation stage utilizing multiple kernels with projections onto hyperslabs and a diffusion stage to achi
Externí odkaz:
http://arxiv.org/abs/1801.07087
Autor:
Ohnishi, Motoya, Yukawa, Masahiro
Publikováno v:
IEEE Trans. Signal Processing. Vol. 66, No. 15, August 1, 2018
We propose a novel online learning paradigm for nonlinear-function estimation tasks based on the iterative projections in the L2 space with probability measure reflecting the stochastic property of input signals. The proposed learning algorithm explo
Externí odkaz:
http://arxiv.org/abs/1712.04573