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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| ISEC, SITE, ICSS, EMM, HWS, BioX, IPSJ-CSEC, IPSJ-SPT [detail] | 2019-07-24 10:55 |
Kochi | Kochi University of Technology | Stochastic Existence Connecting Logos that are not necessarily completely divided and Language Games
-- Limitations of Security Models and the Possibility of Artificial Intelligence -- Tetsuya Morizumi (KU)  ISEC2019-49 SITE2019-43 BioX2019-41 HWS2019-44 ICSS2019-47 EMM2019-52 |
In this paper we describe that AI architecture including input data in artificial intelligence system for Bayesian estim... [more] | ISEC2019-49 SITE2019-43 BioX2019-41 HWS2019-44 ICSS2019-47 EMM2019-52 pp.317-324 |
| IN, NS (Joint) |
2019-03-05 16:00 |
Okinawa | Okinawa Convention Center | Prediction Method for Position of Uncontrollable Vehicle Based on Bayesian Inference in Network-Assisted Autonomous Driving Platform Yuya Taniguchi, Yoshiki Aoki, Satoru Okamoto, Naoaki Yamanaka (Keio Univ.) NS2018-285 |
In recent research, network-assisted autonomous driving vehicle is proposed, which means that autonomous driving is cont... [more] | NS2018-285 pp.527-532 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Inference for Logitistic Regression Mixture Model with Local Variational Approximation and Study for Variational Free Energy Fumito Nakamura, Ryosuke Konishi (Generic Solution), Yasushi Kiyoki (Keio) IBISML2018-48 |
A logistic regression mixture model (LRMM) is a mixed model of the Logistic regression model, and it is widely used in t... [more] | IBISML2018-48 pp.29-36 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Variational Approximation Accuracy in Non-negative Matrix Factorization Naoki Hayashi (MSI) IBISML2018-51 |
The asymptotic behavior of the variational free energy of the non-negative matrix factorization (NMF) has been elucidate... [more] | IBISML2018-51 pp.53-60 |
| 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 | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
A Note on the Estimation Method of Causality Effects based on Statistical Decision Theory Shunsuke Horii, Tota Suko (Waseda Univ.) IBISML2018-97 |
In this paper, we deal with the problem of estimating the intervention effect in statistical causal analysis using struc... [more] | IBISML2018-97 pp.397-402 |
| AI | 2018-08-27 15:50 |
Osaka | Bayesian Inference for Field of Physical Quantity from Data obtained at several Locations Masato Ota, Takeshi Okadome (KG Univ.) AI2018-23 |
This paper proposes a novel method for estimating the physical quantity at every location (physical quan- tity field) fr... [more] | AI2018-23 pp.55-60 |
|
| NS | 2018-04-19 13:25 |
Fukuoka | Fukuoka Univ. | Channel assignment for LPWA networks inspired by perceptual decision-making of human brain Daichi Kominami (Osaka Univ.), Kazuya Suzuki, Yohei Hasegawa, Hideyuki Shimonishi (NEC), Masayuki Murata (Osaka Univ.) NS2018-2 |
Low power wide area (LPWA) technology that realizes low-power-consumption and wide-area communication is rapidly spreadi... [more] | NS2018-2 pp.7-12 |
| MBE, NC (Joint) |
2018-03-14 10:25 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Experimental Analysis of Real Log Canonical Threshold in Stochastic Matrix Factorization using Hamiltonian Monte Carlo Method Naoki Hayashi, Sumio Watanabe (Tokyo Tech) NC2017-89 |
For the real log canonical threshold (RLCT) that gives the Bayesian generalization error of stochastic matrix factorizat... [more] | NC2017-89 pp.127-131 |
| PRMU, MVE, IPSJ-CVIM [detail] | 2018-01-18 09:30 |
Osaka | Trajectory semantic segmentation based on behavior models Daisuke Ogawa, Toru Tamaki, Bisser Raytchev, Kazufumi Kaneda (Hiroshima Univ.) PRMU2017-112 MVE2017-33 |
In many cases, such as trajectories clustering and classification, we often divide a trajectory into segments as preproc... [more] | PRMU2017-112 MVE2017-33 pp.1-7 |
|
| PN | 2017-11-16 15:20 |
Tokyo | Kogakuin Univ. | Virtual Network Reconfiguration Based on Bayesian Attractor Model with Linear Regression Toshihiko Ohba, Shin'ichi Arakawa, Masayuki Murata (Osaka Univ.) PN2017-37 |
A typical approach for configuring a virtual network (VN) over an optical network is to design an optimal VN with a know... [more] | PN2017-37 pp.57-63 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | [Poster Presentation]
Real Log Canonical Threshold of Stochastic Matrix Factorization and its Application to Bayesian Learning Naoki Hayashi, Sumio Watanabe (TokyoTech) IBISML2017-38 |
In stochastic matrix factorization (SMF), we deal with problems that we predict an observed stochastic matrix as a produ... [more] | IBISML2017-38 pp.23-30 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Robust one dimensional phase unwrapping using Markov random fields Yasuhisa Nakashima (Univ. Tokyo), Yasuhiko Igarashi (JST), Yasushi Naruse (NICT), Masato Okada (Univ. Tokyo) IBISML2017-45 |
In the measurement of crustal deformation using satellite or aircraft sensors, interferometric synthetic aperture radar ... [more] | IBISML2017-45 pp.77-84 |
| 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 |
| CQ | 2017-07-27 12:05 |
Hyogo | Kobe University | Time series analysis of failure rates of equipments for telecommunication networks.
-- State space model using Bayesian inference -- Hiroyuki Funakoshi (NTT) CQ2017-35 |
The author has been analyzed the failure rate of telecommunication network equipments by time series analysis using ARIM... [more] | CQ2017-35 pp.37-42 |
| SC | 2017-03-10 15:45 |
Tokyo | National Institute of Informatics | Probabilistic Inference of Customer States Using Statistical Open Data and Bayesian Networks Hiroaki Nakamura, Michiharu Kudo, Hironori Takeuchi (IBM Japan) SC2016-35 |
Enterprises need to provide services specialized for each customer in a timely manner, and for that purpose, they rely o... [more] | SC2016-35 pp.39-44 |
| PN | 2016-11-17 15:05 |
Saitama | KDDI Research, Inc. | A Bayesian-based Virtual Network Reconfiguration in Elastic Optical Path Networks Toshihiko Ohba, Shin'ichi Arakawa, Masayuki Murata (Osaka Univ.) PN2016-33 |
A typical approach for constructing/reconfiguring a virtual network (VN) is to design an optimal topology and the amount... [more] | PN2016-33 pp.45-50 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Inference of Classical Spin Model by Multidimensional Multiple Histogram Method Hikaru Takenaka (UTokyo), Kenji Nagata (UTokyo/AIST/JST), Takashi Mizokawa (Waseda Univ.), Masato Okada (UTokyo/RIKEN) IBISML2016-61 |
We propose a novel method for effective Bayesian inference of classical spin model by the multidimensional multiple hist... [more] | IBISML2016-61 pp.109-116 |
| 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 |
| SS | 2016-03-11 10:50 |
Okinawa | A Prioritization of Combinatorial Testing Using Bayesian Inference Shunya Kawabata (Kyoto Inst. Tech.), Eun-Hye Choi (AIST), Osamu Mizuno (Kyoto Inst. Tech.) SS2015-95 |
An ideal testing detects a large number of faults with a small number of test cases. Combinatorial testing, which focus... [more] |
SS2015-95 pp.115-120 |
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