Masayuki Ohzeki | DeepAI

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Kazuyoshi Yoshimi
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Junya Otsuki
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∙ 09/06/2023

Random postprocessing for combinatorial Bayesian optimization

Model-based sequential approaches to discrete "black-box" optimization, ...

0 Keisuke Morita, et al. ∙

∙ 04/25/2022

Travel time optimization on multi-AGV routing by reverse annealing

Quantum annealing has been actively researched since D-Wave Systems prod...

0 Renichiro Haba, et al. ∙

∙ 01/31/2022

Sparse Signal Reconstruction with QUBO Formulation in l0-regularized Linear Regression

An l0-regularized linear regression for a sparse signal reconstruction i...

0 Naoki Ide, et al. ∙

∙ 03/15/2021

Assessment of image generation by quantum annealer

Quantum annealing was originally proposed as an approach for solving com...

0 Takehito Sato, et al. ∙

∙ 02/24/2021

Kernel-based framework to estimate deformations of pneumothorax lung using relative position of anatomical landmarks

In video-assisted thoracoscopic surgeries, successful procedures of nodu...

0 Utako Yamamoto, et al. ∙

∙ 07/16/2020

Maximum-Likelihood Channel Decoding with Quantum Annealing Machine

We formulate maximum likelihood (ML) channel decoding as a quadratic unc...

0 Naoki Ide, et al. ∙

∙ 02/13/2020

Breaking limitation of quantum annealer in solving optimization problems under constraints

Quantum annealing is a generic solver for optimization problems that use...

0 Masayuki Ohzeki, et al. ∙

∙ 01/21/2019

Message-passing algorithm of quantum annealing with nonstoquastic Hamiltonian

Quantum annealing (QA) is a generic method for solving optimization prob...

0 Masayuki Ohzeki, et al. ∙

∙ 12/04/2018

Control of automated guided vehicles without collision by quantum annealer and digital devices

We formulate an optimization problem to control a large number of automa...

0 Masayuki Ohzeki, et al. ∙

∙ 07/01/2018

Optimization of neural networks via finite-value quantum fluctuations

We numerically test an optimization method for deep neural networks (DNN...

0 Masayuki Ohzeki, et al. ∙

∙ 03/20/2018

Momentum-Space Renormalization Group Transformation in Bayesian Image Modeling by Gaussian Graphical Model

A new Bayesian modeling method is proposed by combining the maximization...

0 Kazuyuki Tanaka, et al. ∙

∙ 11/28/2017

Deformation estimation of an elastic object by partial observation using a neural network

Deformation estimation of elastic object assuming an internal organ is i...

0 Utako Yamamoto, et al. ∙

∙ 02/10/2017

Sparse modeling approach to analytical continuation of imaginary-time quantum Monte Carlo data

A new approach of solving the ill-conditioned inverse problem for analyt...

0 Junya Otsuki, et al. ∙

∙ 02/10/2017

Compressing Green's function using intermediate representation between imaginary-time and real-frequency domains

New model-independent compact representations of imaginary-time data are...

0 Hiroshi Shinaoka, et al. ∙

∙ 11/19/2015

Stochastic gradient method with accelerated stochastic dynamics

In this paper, we propose a novel technique to implement stochastic grad...

0 Masayuki Ohzeki, et al. ∙

∙ 03/11/2015

L_1-regularized Boltzmann machine learning using majorizer minimization

We propose an inference method to estimate sparse interactions and biase...

0 Masayuki Ohzeki, et al. ∙

∙ 01/19/2015

Statistical-mechanical analysis of pre-training and fine tuning in deep learning

In this paper, we present a statistical-mechanical analysis of deep lear...

0 Masayuki Ohzeki, et al. ∙

∙ 01/05/2015

Inverse Renormalization Group Transformation in Bayesian Image Segmentations

A new Bayesian image segmentation algorithm is proposed by combining a l...

0 Kazuyuki Tanaka, et al. ∙

∙ 10/14/2014

Detection of cheating by decimation algorithm

We expand the item response theory to study the case of "cheating studen...

0 Shogo Yamanaka, et al. ∙

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