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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| AP | 2020-02-21 11:25 |
Shizuoka | Shizuoka Univ. Hamamatsu Campus | Random Walk and Wave Propagation Yoshio Karasawa  AP2019-190 |
While doing a random walk, we visit some basic probability distributions that appear in radio wave propagation models. R... [more] | AP2019-190 pp.53-57 |
| SP | 2020-01-29 11:30 |
Toyama | Application of Deep Gaussian Process to Multi-Speaker Text-to-Speech Synthesis using Speaker Codes Kentaro Mitsui, Tomoki Koriyama, Hiroshi Saruwatari (UTokyo) SP2019-49 |
Speaker codes are widely used to achieve multi-speaker text-to-speech synthesis. Conventionally, Deep Neural Network (D... [more] |
SP2019-49 pp.31-36 |
|
| IT, SIP, RCS | 2020-01-23 17:10 |
Hiroshima | Hiroshima City Youth Center | [Invited Talk]
Accelerating Image Processing by Constant-time Filtering Kenjiro Sugimoto (Waseda Univ.) IT2019-59 SIP2019-72 RCS2019-289 |
Digital filter plays a fundamental role in a variety of methods in image processing, computer vision and computer graphi... [more] | IT2019-59 SIP2019-72 RCS2019-289 p.133 |
| IBISML | 2020-01-09 13:50 |
Tokyo | ISM | Dimensionality reduction method for gaussian process posteriors based on information geometry Hideaki Ishibashi (Kyutech), Shotaro Akaho (AIST/RIKEN) IBISML2019-20 |
This paper proposes an extension of principal component analysis for gaussian process posteriors which is denoted by GP-... [more] | IBISML2019-20 pp.17-24 |
| MIKA (2nd) |
2019-10-04 12:15 |
Hokkaido | Hokkaido Univ. | [Invited Lecture]
Performance improvement of 5G IoT wireless communication using nonlinear signal processing Eiji Okamoto (NITech) |
In addition to a traditional use case of high-capacity communication, the fifth generation mobile communications system ... [more] | |
| RCS, SAT (Joint) |
2019-08-22 09:25 |
Aichi | Nagoya University | A Study on a Performance Improvement by Cyclic Redundancy Check for Large-Scale SCMA Detection Renjie Li, Toshihiko Nishimura, Takeo Ohgane, Yasutaka Ogawa, Junichiro Hagiwara (Hokkaido Univ.) RCS2019-147 |
SCMA attracts attention as a non-orthogonal multiple access method for device-to-device communi- cation. Generally, NOMA... [more] | RCS2019-147 pp.7-12 |
| NC, IBISML, IPSJ-MPS, IPSJ-BIO [detail] | 2019-06-17 15:50 |
Okinawa | Okinawa Institute of Science and Technology | A Comparison of Surrogate Models in Bayesian Optimization Sho Shimoyama (Meiji Univ.), Masahiro Nomura (CA) IBISML2019-7 |
Bayesian optimization can efficiently select the next search point by using a surrogate model that estimates an objectiv... [more] | IBISML2019-7 pp.43-50 |
| IT, ISEC, WBS | 2019-03-07 15:05 |
Tokyo | University of Electro-Communications | Innovations, σ-Fields, and Martingales Associated with Viterbi Decoding of Convolutional Codes Masato Tajima IT2018-98 ISEC2018-104 WBS2018-99 |
In the previous paper, by comparing with the results in the linear filtering theory, we have introduced the notion of in... [more] | IT2018-98 ISEC2018-104 WBS2018-99 pp.137-142 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Active learning for identifying local minimum points based on the derivative of Gaussian process Yu Inatsu (RIKEN), Daisuke Sugita (NITech), Kazuaki Toyoura (Kyoto Univ.), Ichiro Takeuchi (NITech/RIKEN/NIMS) IBISML2018-94 |
In many fields such as materials science, knowing local minimum points of unknown functions is important for understand... [more] | IBISML2018-94 pp.373-380 |
| 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 |
|
| 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 |
|
| EA | 2018-02-15 16:15 |
Hiroshima | Pref. Univ. Hiroshima | [Invited Talk]
Signal Processing for Sound Environment System with Non-Gaussian, Nonlinear and Nonstationary Properties
-- My forty-year history at a slow pace -- Akira Ikuta (Prefectural Univ. Hiroshima) EA2017-96 |
In the actual sound environment system, a specific signal shows various types of probability distribution, and the obser... [more] | EA2017-96 p.19 |
| SP, ASJ-H | 2018-01-20 13:25 |
Tokyo | The University of Tokyo | A study on statistical speech synthesis based on GP-DNN hybrid model Tomoki Koriyama, Takao Kobayashi (Tokyo Tech) SP2017-67 |
We propose a novel approach to Gaussian process regression (GPR)-based speech synthesis in this paper. Since the conve... [more] |
SP2017-67 pp.5-10 |
| 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 |
| 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 |
|
| NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] | 2017-06-25 09:30 |
Okinawa | Okinawa Institute of Science and Technology | Expectation Propagation for t-Exponential Family Futoshi Futami, Issei Sato (Univ. of Tokyo/RIKEN), Masashi Sugiyama (RIKEN/Univ. of Tokyo) IBISML2017-6 |
Exponential family distributions are highly useful in machine learning since their calculation can be performed efficien... [more] | IBISML2017-6 pp.179-184 |
| 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 |
| EA, EMM | 2016-11-18 15:10 |
Oita | Compal Hall (Oita) | State Estimation Based on Bayes' Theorem for Sound Environment System with Quantized Observation
-- Introduction of Particle Filter -- Akira Ikuta, Hisako Orimoto, Gerard Gallagher (Prefectural Univ. Hiroshima) EA2016-66 EMM2016-72 |
In this study, a modified particle filter considering non-Gaussian properties of noises is proposed in a form applicable... [more] | EA2016-66 EMM2016-72 pp.107-112 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Policy search based on sample clustering with Gaussian mixture model Taiki Yano, Shinichi Maeda (Kyoto Univ.) IBISML2016-46 |
EM-based Policy Hyper Parameter Exploration (EPHE)(Wang et al., 2016) is a method that kills two birds with one stone; ... [more] | IBISML2016-46 pp.9-15 |
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