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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| EA | 2024-05-22 13:50 |
Online | Online | Determined BSS based on the proximal average of IVA and DNNs Kazuki Matsumoto (Waseda Univ.), Koki Yamada, Kohei Yatabe (TUAT) |
(To be available after the conference date) [more] | |
| DC, CPSY, IPSJ-SLDM, IPSJ-EMB, IPSJ-ARC [detail] | 2024-03-23 11:45 |
Nagasaki | Ikinoshima Hall (Primary: On-site, Secondary: Online) |
Evaluating composition of quantum circuit and learnability in quantum neural network with NISQ devices Naoki Marumo (Waseda Univ.), Yasutaka Wada (Meisei Univ.), Kazunori Ueda, Keiji Kimura (Waseda Univ.)  CPSY2023-52 DC2023-118 |
The more numbers of repeat of Ansatz and the more qubit entangling improve learnability of quantum machine learning by v... [more] | CPSY2023-52 DC2023-118 pp.82-87 |
| IE, MVE, CQ, IMQ (Joint) [detail] |
2024-03-15 09:50 |
Okinawa | Okinawa Sangyo Shien Center (Primary: On-site, Secondary: Online) |
IMQ2023-68 IE2023-123 MVE2023-97 |
We propose a simultaneous method of multimodal graph signal denoising and graph learning. Since sensor networks distribu... [more] | IMQ2023-68 IE2023-123 MVE2023-97 pp.301-306 |
| SIP, SP, EA, IPSJ-SLP [detail] | 2024-02-29 16:20 |
Okinawa | (Primary: On-site, Secondary: Online) |
Comparison of DNN architectures for determined BSS by proximal average of IVA and DNN Kazuki Matsumoto (Waseda Univ.), Koki Yamada, Kohei Yatabe (TUAT) EA2023-88 SIP2023-135 SP2023-70 |
We have proposed a framework called PA-BSS for high-performance separation matrix estimation using deep denoisers based ... [more] | EA2023-88 SIP2023-135 SP2023-70 pp.162-167 |
| SIP, IT, RCS | 2024-01-18 11:45 |
Miyagi | (Primary: On-site, Secondary: Online) |
A Proposal to Improve the Accuracy of BKW Algorithm Yuto Ko, Bagus Santoso (UEC) IT2023-41 SIP2023-74 RCS2023-216 |
The Blum-Kalai-Wasserman (BKW) algorithm is widely known as an algorithm for solving the Learning Parity with Noise (LPN... [more] | IT2023-41 SIP2023-74 RCS2023-216 pp.62-67 |
| SIP, IT, RCS | 2024-01-19 14:30 |
Miyagi | (Primary: On-site, Secondary: Online) |
Infant Detection in Passenger Vehicles Using Millimeter Wave FMCW-MIMO Radar and CFAR Algorithm Kotone Sato, Steven Wandale, Koichi Ichige (Yokohama National Univ.), Kazuya Kimura, Ryo Sugiura (Murata Manufacturing) IT2023-71 SIP2023-104 RCS2023-246 |
This paper implements several proposed features using the CFAR algorithm, then constructs a concise decision tree model ... [more] | IT2023-71 SIP2023-104 RCS2023-246 pp.223-228 |
| QIT (2nd) |
2023-12-17 17:30 |
Okinawa | OIST (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Quantum Circuit Preparation for Loading Probability Distributions Using Label Swapping Yuichi Sano (Kyoto Univ.), Ikko Hamamura (IBM) |
Quantum state preparation is an essential subroutine in many quantum algorithms, such as quantum algorithm for Monte Car... [more] | |
| NS | 2023-10-06 15:20 |
Hokkaido | Hokkaidou University + Online (Primary: On-site, Secondary: Online) |
Incentive Mechanism Considering Heterogeneous Privacy Demand Level in Federated Learning with Differential Privacy Shota Miyagoshi, Takuji Tachibana (Univ. Fukui) NS2023-104 |
In federated learning, where multiple data owners participate as clients to perform machine learning, each client shares... [more] | NS2023-104 pp.162-167 |
| SIP | 2023-08-08 14:40 |
Osaka | Osaka Univ. (Suita) Convention Center (Primary: On-site, Secondary: Online) |
[Invited Talk]
Recent Development on Acoustic Echo Canceller Kenta Iwai (Ritsumeikan Univ.) SIP2023-61 |
In hands-free tele-communication systems, acoustic echo occurs and disturbs the speech communication. To reduce the acou... [more] | SIP2023-61 pp.71-76 |
| EA, ASJ-H, ASJ-MA, ASJ-SP | 2023-07-02 15:10 |
Hokkaido | Speech Restoration of Spectrogram Images Printed in a Document "Visible Speech" Published in 1947 Naofumi Aoki (Hokkaido Univ.) EA2023-6 |
The restoration of speech materials recorded in the past might be regarded as a study in acoustical archeology. It may p... [more] | EA2023-6 pp.12-15 |
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| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2023-06-29 16:25 |
Okinawa | OIST Conference Center (Primary: On-site, Secondary: Online) |
Minorization-Maximization for Determinantal Point Processes Takahiro Kawashima (SOKENDAI), Hideitsu Hino (ISM/RIKEN) NC2023-7 IBISML2023-7 |
A determinantal point process (DPP) is a powerful probabilistic model that generates diverse random subsets from a groun... [more] | NC2023-7 IBISML2023-7 pp.39-47 |
| PN, NS, OCS (Joint) |
2023-06-09 10:50 |
Kagawa | (Primary: On-site, Secondary: Online) |
A New Framework for Improving Inter-protocol Fairness of Congestion Control Algorihtms Satoshi Utsumi, Shogo Okada (Fukushima Univ.), Salahuddin Muhammad Salim Zabir (NIT, Tsuruoka College) NS2023-29 |
CUBIC and BBR are widely used as congestion control algorithms in the current Internet. When deploying new congestion co... [more] | NS2023-29 pp.59-64 |
| QIT (2nd) |
2023-05-30 10:00 |
Kyoto | Katsura Campus, Kyoto University | A new initial distribution for qGAN to load probability distributions Yuichi Sano, Ryosuke Koga (Kyoto Univ.), Masaya Abe, Kei Nakagawa (Nomura Asset Management) |
Quantum computers are gaining attention for their ability to solve certain problems faster than classical computers, and... [more] | |
| QIT (2nd) |
2023-05-29 16:30 |
Kyoto | Katsura Campus, Kyoto University | [Poster Presentation]
Improving the Performance of Quantum GANs through Label Replacement Ryosuke Koga, Yuichi Sano (Kyoto Univ.), Ikko Hamamura (IBM) |
Instead of Monte Carlo simulations for numerical integration, we can use a quantum expectation estimation algorithm, whi... [more] | |
| SS | 2023-03-15 15:15 |
Okinawa | (Primary: On-site, Secondary: Online) |
Attempt to generate source code using previous token type information Shun Tanaka, Yamamoto Tetsuo (Ryukoku Univ.) SS2022-70 |
In this study, we improve the BeamSearch algorithm used in the machine Learning model. We add an process to the token se... [more] | SS2022-70 pp.139-144 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-03 17:10 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
Forest Construction of Gaussian and Discrete Variables based on WBIC Ashraful Islam, Joe Suzuki (Osaka Univ.) PRMU2022-126 IBISML2022-133 |
Mutual information is a metric that determines the association between two random variables by measuring the amount of i... [more] | PRMU2022-126 IBISML2022-133 pp.371-377 |
| RCS, SR, SRW (Joint) |
2023-03-01 14:45 |
Tokyo | Tokyo Institute of Technology, and Online (Primary: On-site, Secondary: Online) |
[Invited Lecture]
Radio Propagation Modeling based on Ray Tracing Simulation for CPS Wireless Emulator Yukiko Kishiki, Kenshi Horihata, Gilbert Siy Ching (KKE) SR2022-85 |
In Society 5.0, cyber-physical space interacts with physical space, and new design, evaluation and verification of radio... [more] | SR2022-85 p.10 |
| NS, ICM, CQ, NV (Joint) |
2022-11-24 10:20 |
Fukuoka | Humanities and Social Sciences Center, Fukuoka Univ. + Online (Primary: On-site, Secondary: Online) |
Social Surplus Maximization Using Incentive Mechanism for Cross-Silo Federated Learning with Differential Privacy Shota Miyagoshi, Takuji Tachibana (Univ. Fukui) NS2022-101 |
In cross-silo federated learning, where multiple organizations participate, the prediction accuracy of the global model ... [more] | NS2022-101 pp.7-12 |
| SeMI, IPSJ-MBL, IPSJ-UBI | 2022-03-07 14:25 |
Online | Online | Non-Backtracking Consensus Algorithm for Sensor Networks Akihito Taya (Aoyama Gakuin Univ.) SeMI2021-86 |
This paper proposes a non-backtracking consensus algorithm for sensor networks. By focusing on the amount of information... [more] | SeMI2021-86 pp.19-24 |
| NLP, MICT, MBE, NC (Joint) [detail] |
2022-01-21 16:00 |
Online | Online | On the Study of Second-Order Training Algorithm using Matrix Diagonalization based on Hutchinson estimation Ryo Yamatomi, Shahrzad Mahboubi, Hiroshi Ninomiya (Shonan Inst. Tec.) NLP2021-89 MICT2021-64 MBE2021-50 |
In this study, we propose a new training algorithm based on the second-order approximated gradient method, which aims to... [more] | NLP2021-89 MICT2021-64 MBE2021-50 pp.67-70 |
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