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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MIKA (3rd) |
2023-10-10 15:35 |
Okinawa | Okinawa Jichikaikan (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Investigation of Ultra-Fast Beam Selection Optimization Method Based on Ising Model Naganuma Shunta, Fujita Tsukumo, Arai Maki (Tokyo Univ. of Science), Li Aohan (The Univ. of Electro-Communications), Hasegawa Mikio (Tokyo Univ. of Science) |
The Ising model is a mathematical model that represents the spin interactions of microscopic components of a substance. ... [more] | |
| IT | 2022-07-22 10:50 |
Okayama | Okayama University of Science (Primary: On-site, Secondary: Online) |
A study on deep learning-based cyber attack detection Ruei-Fong Hong, Qiangfu Zhao (UoA), Shih-Cheng Horng (CYUT)  IT2022-22 |
Cyberattack is a broad term for cybercrime that includes any deliberate attack on a computer device, network or infrastr... [more] | IT2022-22 pp.36-41 |
| EA | 2022-05-13 12:45 |
Online | Online | Directionally-weighted region-to-region kernel interpolation of acoustic transfer function Juliano G. C. Ribeiro, Shoichi Koyama, Hiroshi Saruwatari (UTokyo) EA2022-4 |
An interpolation method for the acoustic transfer function (ATF) for variable source and receiver points within regions ... [more] | EA2022-4 pp.18-19 |
| R | 2021-07-17 14:25 |
Online | Virtual | Refined Ensemble Learning Algorithms for Software Bug Prediction
-- Metaheuristic Approach -- Keisuke Fukuda, Tadashi Dohi, Hiroyuki Okamura (Hiroshima Univ.) R2021-19 |
In this paper, we propose to apply three metaheuristic algorithms; latin hypercube sampling, ABC (artificial bee colon... [more] |
R2021-19 pp.18-23 |
| PRMU, IPSJ-CVIM | 2020-03-17 09:45 |
Kyoto | (Cancelled but technical report was issued) |
Deep neural network representation and learning of low-rank and sparse approximation
-- With application to celiac angiography under free breathing -- Ryohei Miyoshi, Tomoya Sakai (Nagasaki Univ.), Takashi Ohnishi, Hideaki Haneishi (Chiba Univ.) PRMU2019-91 |
Low-rank and sparse (L+S) approximation, a.k.a. stable and robust principal component analysis, is known to be suitable ... [more] | PRMU2019-91 pp.133-138 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Proposal of Hyperparameter Optimization Framework Using a Non-Stationary Multi-Armed Bandit Algorithm Kenshi Abe, Masahiro Nomura (CA) IBISML2018-62 |
Hyperparameter optimization problem is an important problem that appears in areas such as machine learning. Hyperparamet... [more] | IBISML2018-62 pp.135-142 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Lattice Model Selection of Gaussian Markov Random Field Hirosato Ito, Hirotaka Sakamoto, Shun Katakami, Masato Okada (Univ. Tokyo) IBISML2018-69 |
Recently, many observed images have been obtained in various natural scientific fields. It is very important issue to e... [more] | IBISML2018-69 pp.191-196 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Hyperparameter distribution estimation for binary images with the exchange Monte Carlo method Koki Obinata, Shun Katakami, Yue Yonghao, Masato Okada (UTokyo) IBISML2018-79 |
We estimate the distribution of hyperparameters corresponding to the coupling constant and noise in- tensity from an Isi... [more] | IBISML2018-79 pp.263-270 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | Approximated hyperparameter distribution estimation using Gaussian process and Bayesian optimization Shun Katakami, Hirotaka Sakamoto, Masato Okada (UTokyo) IBISML2017-81 |
In order to reduce the computational cost of Bayesian inference, we propose a method to estimate the Bayesian posterior ... [more] | IBISML2017-81 pp.333-338 |
| IBISML | 2016-11-17 14:00 |
Kyoto | Kyoto Univ. | Gaussian Markov random field model without periodic boundary conditions Shun Katakami, Hirotaka Sakamoto, Shin Murata, Masato Okada (UTokyo) IBISML2016-83 |
In this study, we discuss Gaussian Markov random field model without periodic boundary conditions. First, we formulate a... [more] | IBISML2016-83 pp.267-274 |
| NC, MBE | 2015-03-17 13:25 |
Tokyo | Tamagawa University | Effects of downsampling on hyperparameter estimation for Markov random field model Hirotaka Sakamoto (Univ. Tokyo), Yoshinori Nakanishi-Ohno (Univ. Tokyo/JSPS), Masato Okada (Univ. Tokyo/RIKEN) MBE2014-174 NC2014-125 |
We investigate effects which downsampling has on latent-variable estimation from image data. Downsampling is essential f... [more] | MBE2014-174 NC2014-125 pp.325-330 |
| NC, MBE | 2015-03-17 13:50 |
Tokyo | Tamagawa University | Optimization of LASSO Learning using WAIC and Its Application to City Data Analysis Dai Miyazaki, Sumio Watanabe (Tokyo Tech) MBE2014-175 NC2014-126 |
LASSO(Least Absolute Shrinkage and Selection Operator) is a method adding a penalty term consisting of absolute values o... [more] | MBE2014-175 NC2014-126 pp.331-336 |
| IBISML | 2014-11-18 15:00 |
Aichi | Nagoya Univ. | [Poster Presentation]
Optimization Method of LASSO Hyperparameter using WAIC Dai Miyazaki, Sumio Watanabe (Tokyo Tech) IBISML2014-63 |
LASSO (Least Absolute Shrinkage and Selection Operator) was proposed as a regression method using a penalty term made of... [more] | IBISML2014-63 pp.213-218 |
| IBISML | 2014-11-18 15:00 |
Aichi | Nagoya Univ. | [Poster Presentation]
Unsupervised Dimension Reduction via Least-Squares Quadratic Mutual Information Janya Sainui (Tokyo Inst. of Tech.), Masashi Sugiyama (Univ. of Tokyo) IBISML2014-69 |
The goal of dimension reduction is to represent high-dimensional data in a lower-dimensional subspace, while intrinsic p... [more] | IBISML2014-69 pp.259-262 |
| NC, MBE (Joint) |
2012-12-12 15:40 |
Aichi | Toyohashi University of Technology | Distribution estimation of hyperparameters in Markov random field model Yoshinori Ohno, Kenji Nagata (Univ. Tokyo), Hayaru Shouno (UEC), Masato Okada (Univ. Tokyo/RIKEN) NC2012-86 |
Recent advances in measurement techniques allow us to obtain a large quantity of imaging data in various natural science... [more] | NC2012-86 pp.55-60 |
| IBISML | 2012-03-12 11:50 |
Tokyo | The Institute of Statistical Mathematics | Hyperparameter Selection of Infinite Gaussian Mixture Model via Widely Applicable Information Criterion Takushi Miki, Masahiro Kohjima, Sumio Watanabe (titech) IBISML2011-91 |
Recently, nonparametric Bayesian method is applied to wide range of research fields such as natural language processing,... [more] | IBISML2011-91 pp.29-33 |
| MBE, NC (Joint) |
2011-11-24 16:10 |
Miyagi | ECEI Departments, Graduate School of Engineering, Tohoku University | An image restoration method for Poisson observation using a latent variational approximation Hayaru Shouno (UEC), Ken Takiyama, Masato Okada (The Univ. of Tokyo) NC2011-73 |
In this study, we treat an image restoration problem throughout a Poisson noise channel observation. The Poisson noise c... [more] | NC2011-73 pp.11-16 |
| MBE, NC (Joint) |
2011-11-24 16:35 |
Miyagi | ECEI Departments, Graduate School of Engineering, Tohoku University | A deterministic algorithm for hyperparameter estimation in nonlinear Markov random field model Yoshinori Ohno, Kenji Nagata (Univ. Tokyo), Hayaru Shouno (U.E.C.), Masato Okada (Univ. Tokyo/RIKEN) NC2011-74 |
Image restoration widely used in natural science is often formulated by Markov random field (MRF) model. In MRF model, t... [more] | NC2011-74 pp.17-22 |
| IBISML | 2011-11-10 15:45 |
Nara | Nara Womens Univ. | Image Segmentation and Restoration using Switching State-Space Model and Variational Bayesian Method Ryota Hasegawa (Kansai Univ.), Ken Takiyama, Masato Okada (Univ. of Tokyo), Seiji Miyoshi (Kansai Univ.) IBISML2011-67 |
We derive a deterministic algorithm that restores and segments image using switching state-space model and variational B... [more] | IBISML2011-67 pp.169-174 |
| NC, MBE (Joint) |
2011-03-08 10:40 |
Tokyo | Tamagawa University | Effect of Information Source on Cross Validation in Variational Bayes Learning Shinji Oyama, Sumio Watanabe (Tokyo Tech.) NC2010-167 |
The variational Bayes learning provides high generalization performance as the Bayes learning using a small computationa... [more] | NC2010-167 pp.235-240 |
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