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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| RCS | 2022-06-17 09:40 |
Okinawa | University of the Ryukyus, Senbaru Campus and online (Primary: On-site, Secondary: Online) |
A Study on Expectation Propagation-based Iterative Detection for Overloaded MIMO in Coded Systems Takuma Kobayashi, Takumi Takahashi, Kenta Ito (Osaka Univ.), Shinsuke Ibi (Doshisha Univ.), Seiichi Sampei (Osaka Univ.)  RCS2022-57 |
This paper investigates an appropriate design for iterative detection receivers in overloaded large multi-input and mult... [more] | RCS2022-57 pp.194-199 |
| CCS, NLP | 2022-06-10 10:55 |
Osaka | (Primary: On-site, Secondary: Online) |
[Invited Talk]
Computation and learning based on dual stochasticity of the brain Jun-nosuke Teramae (Kyoto Univ.) NLP2022-16 CCS2022-16 |
Neurons and synapses in the brain are highly stochastic devices. Neurons responsible for signal propagation in the brain... [more] | NLP2022-16 CCS2022-16 pp.78-83 |
| NLP, CAS | 2019-10-22 15:55 |
Gifu | Gifu Univ. | Discussion on Modeling Nano-Carbon Electrons by Classical Probabilistic Particles Shota Hayakawato, Ibuki Nakamura, Hisato Fujisaka (Hiroshima City Univ.) CAS2019-35 NLP2019-75 |
The behavior of electrons on graphene is described by massless Dirac equation whose solution is a quantum wave with pseu... [more] | CAS2019-35 NLP2019-75 pp.61-66 |
| QIT (2nd) |
2018-11-26 09:50 |
Tokyo | The University of Tokyo | Non-Uniform State Complexity of Quantum Finite Automata and Quantum Polynomial-Time Logarithmic-Space Computation with Quantum Advice
-- (Preliminary Report) -- Tomoyuki Yamakami (U of Fukui) |
The state complexity of a finite(-state) automaton intuitively measures the size of the description of the automaton. Sa... [more] | |
| COMP, IPSJ-AL | 2018-05-25 14:30 |
Aichi | Nagoya Institute of Technology | Fast Aggregation in Probabilistic Population Protocols Ryota Eguchi, Taisuke Izumi (Nagoya Inst. of Tech.) COMP2018-6 |
In this paper, we propose a new aggregation algorithm in probabilistic population protocol model with a base station. Sp... [more] | COMP2018-6 pp.79-84 |
| NLP | 2018-04-26 10:20 |
Kumamoto | Kumaoto Univ. | A Probabilistic Lattice Model of a Three-Port Quantum Wave Filter Shota Hayakawa, Hisato Fujisaka, Takeshi Kamio (Hiroshima City Univ.) NLP2018-1 |
Quantum potential systems, especially a system functioning as a three-port quantum wave filter, are modeled after lattic... [more] | NLP2018-1 pp.1-4 |
| RCS, SAT (Joint) |
2017-08-17 13:05 |
Niigata | Niigata Univ. | [Invited Lecture]
A Study on Belief Propagation of Multi-Dimensional Signal Detection for Higher-Order Modulation Shinsuke Ibi, Takumi Takahashi, Seiichi Sampei (Osaka Univ.) SAT2017-23 RCS2017-153 |
This paper proposes a novel design of the hybrid structure of probabilistic data association (PDA) and Gaussian belief p... [more] | SAT2017-23 RCS2017-153 pp.19-24(SAT), pp.61-66(RCS) |
| TL | 2016-07-24 13:45 |
Tokyo | Waseda Univ. (Nishi-Waseda Campus) | Computational modeling of hierarchical morphological structures Yohei Oseki, Alec Marantz (NYU) TL2016-24 |
Chomsky (1956, 1957) famously showed that sentences are organized as hierarchical structures, not linear strings, of wor... [more] | TL2016-24 pp.65-69 |
| VLD, CAS, MSS, SIP | 2016-06-16 11:20 |
Aomori | Hirosaki Shiritsu Kanko-kan | Hardware Implementation of Stochastic Gammatone Filter Naoya Onizawa, Shunsuke Koshita, Shuichi Sakamoto, Masahide Abe, Masayuki Kawamata, Takahiro Hanyu (Tohoku Univ.) CAS2016-6 VLD2016-12 SIP2016-40 MSS2016-6 |
This paper presents a design of a gammatone filter based on stochastic computation. The gammatone filter well expresses ... [more] | CAS2016-6 VLD2016-12 SIP2016-40 MSS2016-6 pp.29-34 |
| IN, IA (Joint) |
2015-12-17 10:20 |
Hiroshima | Hiroshima City University | Performance Evaluation of Location-Based Intermittent Ad Hoc Communication with Probabilistic Algorithm Sho Kumagai, Hiroaki Higaki (Tokyo Denki Univ.) IN2015-71 |
For reduction of battery consumption in wireless nodes in wireless adhoc networks, intermittent communication is introdu... [more] | IN2015-71 pp.1-6 |
| ISEC, IT, WBS | 2015-03-02 15:10 |
Fukuoka | The University of Kitakyushu | A Study of Two-dimensional Antidictionary Coding Using Automata Takahiro Ota (NPIT), Hiroyoshi Morita (UEC) IT2014-78 ISEC2014-91 WBS2014-70 |
In one dimension, for an input string, an antidictionary is the set of all the minimal strings which do not appear in th... [more] | IT2014-78 ISEC2014-91 WBS2014-70 pp.103-109 |
| DC | 2014-10-27 14:55 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Dynamic Switching of Job Selection Methods in Volunteer Computing Shinya Yasuda, Kan Watanabe (Okayama Univ.), Masaru Fukushi (Yamaguchi Univ.), Yasuyuki Nogami (Okayama Univ.) DC2014-22 |
This paper proposes a dynamic switching method of job selection methods in volunteer computing (VC) systems, where the c... [more] | DC2014-22 pp.5-10 |
| MBE, NC (Joint) |
2012-11-17 13:30 |
Miyagi | Tohoku University | Composite likelihood estimation for bipartite Boltzmann machines Takashi Asari, Muneki Yasuda, Yuji Waizumi, Kazuyuki Tanaka (Tohoku Univ.) NC2012-68 |
The recent development of information technology has enabled us to obtain and to storage huge information data. Because... [more] |
NC2012-68 pp.39-44 |
| IBISML | 2011-11-09 15:45 |
Nara | Nara Womens Univ. | Gaussian FoE model with correlations among color components Ryuta Murayama, Muneki Yasuda, Yuji Waizumi, Kazuyuki Tanaka (Tohoku Univ.) IBISML2011-58 |
Color images consist of some color components such as RGB, YCbCr. They have been often treated independently in usual im... [more] | IBISML2011-58 pp.105-111 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2011-09-05 10:00 |
Hokkaido | Global Solution of Variational Bayesian Matrix Factorization Under Matrix-wise Independence Shinichi Nakajima (Nikon), Masashi Sugiyama (Tokyo Inst. of Tech.), Derin Babacan (Illinois Univ.) PRMU2011-58 IBISML2011-17 |
Variational Bayesian matrix factorization (VBMF) efficiently approximates the posterior distribution of factorized mat... [more] |
PRMU2011-58 IBISML2011-17 pp.1-8 |
|
| NC | 2011-07-26 09:30 |
Hyogo | Graduate School of Engineering, Kobe University | Statistical estimation of non-uniform dendritic membrane properties Toshiaki Omori (Univ. of Tokyo/RIKEN), Toru Aonishi (Tokyo Tech.), Masato Okada (Univ. of Tokyo/RIKEN) NC2011-32 |
We propose a statistical algorithm for extracting non-uniform dendritic membrane properties from partially observable no... [more] | NC2011-32 pp.65-70 |
| IBISML | 2011-03-29 10:30 |
Osaka | Nakanoshima Center, Osaka Univ. | On Automatic Dimensionality Selection in Probabilistic PCA Shinichi Nakajima (Nikon), Masashi Sugiyama (Tokyo Inst. of Tech./JST), Derin Babacan (Illinois of.Univ.) IBISML2010-123 |
In probabilistic PCA, the fully Bayesian estimation is computationally intractable. To cope with this problem, two ty... [more] |
IBISML2010-123 pp.131-138 |
| NLP | 2010-12-13 11:00 |
Tottori | Yonago Convention Center | An All-Coupled Probabilistic Network Model of Delay-Locked Loops Keisuke Nagata, Hisato Fujisaka, Takeshi Kamio, Kazuhisa Haeiwa (Hiroshima City Univ.) NLP2010-115 |
In this paper, we build a probabilistic 2J+1-site network which is a model of a delay-locked loop (DLL) in a CDMA commun... [more] | NLP2010-115 pp.21-26 |
| IBISML, PRMU, IPSJ-CVIM [detail] | 2010-09-05 10:40 |
Fukuoka | Fukuoka Univ. | Improving the Accuracy of Least-Squares Probabilistic Classifiers Makoto Yamada, Masashi Sugiyama (Tokyo Inst. of Tech.), Gordon Wichern (MIT Lincoln Lab), Jaak Simm (Tokyo Inst. of Tech.) PRMU2010-60 IBISML2010-32 |
Least-Squares Probabilistic Classifier (LSPC) has been demonstrated to be a computationally-efficient and accurate class... [more] | PRMU2010-60 IBISML2010-32 pp.45-50 |
| KBSE, SS | 2010-05-28 15:20 |
Kyoto | Doshisha University, Imadegawa Campus | Implementation and Evaluation of Probabilistic Timed Real-Example Guided Abstraction Refinement Takaya Shimizu, Masaki Takahashi, Satoshi Yamane (Kanazawa Univ.) SS2010-14 KBSE2010-14 |
Probabilistic Timed REGAR (Real-Example Guided Abstraction Refinement) is a subclass of PTCTL(Probabilistic Timed Comput... [more] | SS2010-14 KBSE2010-14 pp.81-86 |
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