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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2018-06-13 10:00 |
Okinawa | Okinawa Institute of Science and Technology | Active Level Set Estimation with Multi-fidelity Evaluations Shion Takeno (Nitech), Hitoshi Fukuoka (Nagoya Univ.), Yuhki Tsukada (Nagoya Univ./JST), Toshiyuki Koyama (Nagoya Univ.), Motoki Shiga (Gifu Univ./JST/RIKEN), Ichiro Takeuchi (NITech/NIMS/RIKEN), Masayuki Karasuyama (NITech/NIMS/JST)  IBISML2018-1 |
Level set estimation is a problem to identify a level set of an unknown function, which is defined by whether the functi... [more] | IBISML2018-1 pp.1-8 |
| SIP, EA, SP, MI (Joint) [detail] |
2018-03-19 10:50 |
Okinawa | On the Use of Deep Gaussian Processes for GPR-based Speech Synthesis Tomoki Koriyama, Takao Kobayashi (Tokyo Inst. of Tech.) EA2017-106 SIP2017-115 SP2017-89 |
This paper proposes a speech synthesis framework based on deep Gaussian processes (DGPs). DGP is a Bayesian deep learn... [more] |
EA2017-106 SIP2017-115 SP2017-89 pp.27-32 |
|
| PRMU, IBISML, IPSJ-CVIM [detail] | 2017-09-15 10:30 |
Tokyo | Experimental Analysis of Variational Bayesian Method in Model Selection of Gaussian Mixture Model by Singular Bayesian Information Criterion Naoki Hayashi (Tokyo Tech), Fumito Nakamura (Bosch) PRMU2017-41 IBISML2017-13 |
A Gaussian mixture model (GMM) is a statistical model used in various fields such a pattern recognition, thus, it is imp... [more] | PRMU2017-41 IBISML2017-13 pp.19-26 |
|
| PRMU, IBISML, IPSJ-CVIM [detail] | 2017-09-15 13:00 |
Tokyo | On MDL Learning of Gaussian Mixture Modlels Kohei Miyamoto, Masanori Kawakita, Jun'ichi Takeuchi (Kyushu Univ.) PRMU2017-47 IBISML2017-19 |
The final goal of this work is model sellection for gaussian mixture models(GMM) based on the minimum description length... [more] | PRMU2017-47 IBISML2017-19 pp.59-66 |
|
| PRMU, IBISML, IPSJ-CVIM [detail] | 2017-09-15 13:30 |
Tokyo | Fast and General-Purpose Bayesian Optimization using Tree-Based Model with Gaussian Process Hiroo Iwanaga (Univ. of Tokyo/NTT DATA MSI), Yukio Ohsawa (Univ. of Tokyo) PRMU2017-48 IBISML2017-20 |
Bayesian optimization is an effective method for black-box optimization problems such as hyperparameter tuning of machin... [more] | PRMU2017-48 IBISML2017-20 pp.67-74 |
|
| IT | 2017-09-08 14:50 |
Yamaguchi | Centcore Yamaguchi Hotel | On Two Part Coding of Gaussian Mixture Models Kohei Miyamoto, Masanori Kawakita, Jun'ichi Takeuchi (Kyushu Univ.) IT2017-47 |
The final goal of this work is model sellection for gaussian mixture models(GMM) based on the minimum description leng... [more] |
IT2017-47 pp.49-54 |
| NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] | 2017-06-25 11:25 |
Okinawa | Okinawa Institute of Science and Technology | Cost-sensitive Bayesian optimization for multiple objectives and its application to material science Tomohiro Yonezu (NITech), Tomoyuki Tamura, Ryo Kobayashi (NITech/NIMS), Ichiro Takeuchi (NITech/NIMS/RIKEN), Masayuki Karasuyama (NITech/NIMS/JST) IBISML2017-10 |
We consider solving a set of black-box optimization problems in which each problem has a similar objective function each... [more] | IBISML2017-10 pp.207-213 |
| SP | 2016-08-24 16:15 |
Kyoto | ACCMS, Kyoto Univ. | [Poster Presentation]
Joint Enhancement of Spectral and Cepstral Sequences of Noisy Speech Li Li (Univ.Tsukuba), Hirokazu Kameoka, Takuya Higuchi (NTT), Hiroshi Saruwatari (Univ.Tokyo), Shoji Makino (Univ.Tsukuba) SP2016-32 |
While spectral domain speech enhancement algorithms using non-negative matrix factorization (NMF) are powerful in terms ... [more] | SP2016-32 pp.29-32 |
| EMM, ISEC, SITE, ICSS, IPSJ-CSEC, IPSJ-SPT [detail] | 2016-07-15 13:00 |
Yamaguchi | Efficient Discrete Gaussian Sampling on Constrained Devices Yuki Tanaka, Isamu Teranishi, Kazuhiko Minematsu (NEC), Yoshinori Aono (NICT) ISEC2016-32 SITE2016-26 ICSS2016-32 EMM2016-40 |
Lattice-based cryptography has been attracted by features of simple-implementation, quantum-resilient, and high-level fu... [more] | ISEC2016-32 SITE2016-26 ICSS2016-32 EMM2016-40 pp.169-175 |
|
| SR, SRW (Joint) |
2016-05-17 14:00 |
Overseas | Hotel Lasaretti, Oulu, Finland | [Poster Presentation]
QR-Decomposed Subgraph Belief Propagation for Large MIMO Systems Shogo Tanabe, Koji Ishibashi (Univ. Electro-Comm.) SR2016-20 SRW2016-17 |
In this paper, we discuss a novel low-complexity detection based on belief propagation (BP) algorithm for large-scale mu... [more] | SR2016-20 SRW2016-17 pp.71-72(SR), pp.59-60(SRW) |
| IBISML | 2015-11-27 14:00 |
Ibaraki | Epochal Tsukuba | [Poster Presentation]
Adaptive Objective Function of ICA by Gaussian Approximation in Second-Order Polynomial Feature Space Yoshitatsu Matsuda, Kazunori Yamaguchi (Univ. of Tokyo) IBISML2015-91 |
In this paper, we propose an objective function of ICA with adaptive estimation of the kurtoses of sources. It is deriv... [more] |
IBISML2015-91 pp.285-292 |
| AP, RCS, WPT, SAT (Joint) |
2015-11-04 12:15 |
Okinawa | Okinawa Prefectural Museum & Art Museum | A study on performance improvement of chaos MIMO scheme using advanced stochastic characteristics Eiji Okamoto (NITech) RCS2015-195 |
Chaos multiple-input multiple-output (C-MIMO) scheme is a transmission scheme with Gaussian modulation, where physical-l... [more] | RCS2015-195 pp.31-36 |
| WIT, SP, ASJ-H, PRMU | 2015-06-19 10:00 |
Niigata | Study on prediction of quasiperiodic nonlinear phenomena based on Gaussian process state space model Akira Tamamori, Tomoko Matsui (ISM), Masumi Kitazawa (QOL) PRMU2015-49 SP2015-18 WIT2015-18 |
Many non-linear phenomena which exhibit quasiperiodic fluctuations can be widely observed in the world; population of or... [more] | PRMU2015-49 SP2015-18 WIT2015-18 pp.101-106 |
|
| SIP, EA, SP | 2015-03-03 09:00 |
Okinawa | [Poster Presentation]
Image Interpolation based on Weighting Function of Gaussian Takuro Yamaguchi, Yasuhiro Nakajima, Masaaki Ikehara (Keio Univ.) EA2014-105 SIP2014-146 SP2014-168 |
In this paper, we propose a new image interpolation method based on a 2-D piecewise stationary autoregressive (PAR) mode... [more] | EA2014-105 SIP2014-146 SP2014-168 pp.181-185 |
|
| SP, IPSJ-SLP (Joint) |
2014-07-25 13:20 |
Iwate | Hotel Hanamaki | [Invited Talk]
Evaluation Criteria of Statistical Learning when Gaussian Approximation can not be Applied to Likelihood Function Sumio Watanabe (Tokyo Inst. of Tech.) SP2014-68 |
Conventional statistical asymptotic theory was established based on the assumption that the likelihood function can be a... [more] | SP2014-68 pp.31-36 |
| QIT (2nd) |
2013-11-18 - 2013-11-19 |
Tokyo | Waseda Univ. | [Poster Presentation]
Negative Wigner fuction light generated by exciton-polariton condensates Cristian Joana (NII), Peter van Loock (JGU), Tim Byrnes (NII) |
Exciton-polaritons are bosonic quasi-particles originating from the strong coupling of a cavity photon and a quantum wel... [more] | |
| SIP | 2013-08-29 16:10 |
Tokyo | Tokyo University of Agriculture and Technology | [Tutorial Lecture]
Tensor-Based Machine Learning: Modeling, Algorithms and Applications Qibin Zhao, Andrzej Cichocki (RIKEN) SIP2013-73 |
Tensors are a generalization of vectors and matrices to higher dimensions that can naturally represent the multidimensio... [more] | SIP2013-73 pp.35-40 |
| MBE, NC (Joint) |
2013-03-15 10:15 |
Tokyo | Tamagawa University | Bayesian inference for GTM using non-stationary Gaussian process Nobuhiko Yamaguchi (Saga Univ.) NC2012-168 |
Generative Topographic Mapping (GTM) is a nonlinear topographically preserving mapping from latent to data space introdu... [more] | NC2012-168 pp.197-202 |
| SIS | 2013-03-07 16:20 |
Shizuoka | Create Hamamatsu | Robust Trilateral Filter Using Order Statistics of Pixel Values Tadahiro Azetsu (Yamaguchi Prefectural Univ.), Noriaki Suetake, Eiji Uchino (Yamaguchi Univ.) SIS2012-59 |
The bilateral filter can remove Gaussian noise while preserving edges of the objects in an image. However the bilateral ... [more] | SIS2012-59 pp.75-78 |
| SP, IPSJ-SLP | 2012-12-21 10:15 |
Tokyo | TITECH(Ookayama) | Interpolation of unlearned position based on local regression for single-channel talker localization using acoustic transfer function Ryoichi Takashima, Tetsuya Takiguchi, Yasuo Ariki (Kobe Univ.) SP2012-92 |
This paper presents a sound source (talker) localization method using only a single microphone. In our previous work, we... [more] | SP2012-92 pp.75-80 |
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