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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SIP, IT, RCS | 2024-01-18 13:15 |
Miyagi | (Primary: On-site, Secondary: Online) |
[Invited Talk]
A Revisit to Proximal Decoding for LDPC codes Tadashi Wadayama (NiTech)  IT2023-42 SIP2023-75 RCS2023-217 |
In this invited talk, our proposed algorithm, {em proximal decoding}, is revisited and some optimization-based decodi... [more] |
IT2023-42 SIP2023-75 RCS2023-217 p.68 |
| IBISML | 2023-12-20 15:20 |
Tokyo | National Institute of Informatics (Primary: On-site, Secondary: Online) |
Stabilization and Acceleration of Stochastic Gradient Descent Based on Eigenvalue Decomposition of the Fisher Information Matrix Masazumi Iida, Yoshinari Takeishi (Kyushu Univ.), Siyang Wang (Umea Univ.), Jun'ichi Takeuchi (Kyushu Univ.) IBISML2023-32 |
By using approximated eigen-decomposition of the Fisher information matrix, the Stabilizer method was recently developed... [more] | IBISML2023-32 pp.13-17 |
| EMM, IT | 2023-05-12 09:50 |
Kyoto | Rakuyu Kaikan (Kyoto Univ. Yoshida-South Campus) (Primary: On-site, Secondary: Online) |
Relation on Unconstrained Convex Quadratic Programming between Gradient Method and Numerical Discretization of Gradient Flow Ayano Nakai-Kasai, Tadashi Wadayama (NITech) IT2023-7 EMM2023-7 |
This paper proves one of the properties in terms of gradient descent method and gradient flow. For unconstrained convex ... [more] | IT2023-7 EMM2023-7 pp.31-36 |
| EMM, IT | 2023-05-12 10:15 |
Kyoto | Rakuyu Kaikan (Kyoto Univ. Yoshida-South Campus) (Primary: On-site, Secondary: Online) |
Gradient flow decoding for LDPC codes Tadashi Wadayama, Kensho Nakajima, Ayano Nakai-Kasai (NiTech) IT2023-8 EMM2023-8 |
The power consumption of the integrated circuit is becoming a significant burden, particularly for large-scale signal pr... [more] | IT2023-8 EMM2023-8 pp.37-42 |
| HWS, VLD | 2023-03-01 15:20 |
Okinawa | (Primary: On-site, Secondary: Online) |
A fast SRAF optimization using Voronoi diagram and LUT based intensity evaluation Sota Saito, Yu Horimoto, Atsushi Takahashi (Tokyo Tech), Yukihide Kohira (Univ. of Aizu), Chikaaki Kodama (KIOXIA) VLD2022-80 HWS2022-51 |
Recent advances in technology nodes have led to problems in optical lithography such as reduced fidelity of transferred ... [more] | VLD2022-80 HWS2022-51 pp.43-48 |
| RCS, SR, SRW (Joint) |
2023-03-02 10:50 |
Tokyo | Tokyo Institute of Technology, and Online (Primary: On-site, Secondary: Online) |
Precoder Optimization Using Correlation among Data for Wireless Data Aggregation Naoyuki Hayashi, Ayano Nakai-Kasai, Tadashi Wadayama (NIT) RCS2022-273 |
In this paper, we discuss precoder optimization in wireless data aggregation. Formulate the problem of minimizing the ... [more] |
RCS2022-273 pp.149-154 |
| QIT (2nd) |
2022-12-08 14:00 |
Kanagawa | Keio Univ. (Primary: On-site, Secondary: Online) |
[Poster Presentation]
QestOptPOVM: Numerical search for finding an optimal POVM for multiparameter estimation Jianchao Zhang, Jun Suzuki (UEC) |
It is of fundamental question to find an optimal measurement (POVM) that extracts the most information available about t... [more] | |
| MBE, NC | 2022-12-03 15:25 |
Osaka | Osaka Electro-Communication University | Stabilization and Acceleration of Gradient Descent Based on Eigenvalue Decomposition of the Fisher Information Matrix Jun'ichi Takeuchi, Yoshinari Takeishi, Masazumi Iida (Kyushu Univ.), Noboru Murata (Waseda Univ.), Kazushi Mimura (Hiroshima City Univ.), Hiroshi Nagaoka (Univ. of Electro Communication) MBE2022-39 NC2022-61 |
We propose a method to stabilize the gradient decent method without decreasing learning rate for two-layer neural networ... [more] | MBE2022-39 NC2022-61 pp.80-85 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM [detail] | 2022-11-30 10:20 |
Kumamoto | (Primary: On-site, Secondary: Online) |
A fast SRAF optimization used LUT based point intensity calculation Sota Saito, Atsushi Takahashi (Tokyo Tech) VLD2022-40 ICD2022-57 DC2022-56 RECONF2022-63 |
Recent advances in technology nodes have led to problems in optical lithography such as reduced fidelity of transferred ... [more] | VLD2022-40 ICD2022-57 DC2022-56 RECONF2022-63 pp.121-126 |
| ITE-BCT, OCS, IEE-CMN, OFT | 2022-11-11 13:00 |
Miyagi | Forest-Sendai (Primary: On-site, Secondary: Online) |
Learning-based digital back propagation considering cross-phase modulation in wavelength-division multiplexed transmission systems Takashi Inoue, Ryosuke Matsumoto, Shu Namiki (AIST) OCS2022-44 |
We propose a scheme to compensate waveform distortion of optical signals due to fiber nonlinearity in wavelength-divisio... [more] | OCS2022-44 pp.24-29 |
| SAT, RCS (Joint) |
2022-08-26 11:15 |
Hokkaido | (Primary: On-site, Secondary: Online) |
RCS2022-123 |
(To be available after the conference date) [more] | RCS2022-123 pp.150-155 |
| RCS, SIP, IT | 2022-01-21 11:20 |
Online | Online | Deep-Unfolded Sparse Signal Recovery Algorithm using TopK Operator Masanari Mizutani (NITech), Satoshi Takabe (TITech), Tadashi Wadayama (NITech) IT2021-72 SIP2021-80 RCS2021-240 |
Compressed sensing for estimating sparse signals is formulated as an NP-hard problem, where LASSO based on convex relax... [more] | IT2021-72 SIP2021-80 RCS2021-240 pp.245-251 |
| IT | 2021-07-09 13:25 |
Online | Online | A Note on the Reduction of Computational Complexity for Linear Regression Model Including Cluster Explanatory Variables and Regression Explanatory Variables
-- Bayes Optimal Prediction and Sub-Optimal Algorithm -- Sho Kayama (Waseda Univ.), Shota Saito (Gunma Univ.), Toshiyasu Matsushima (Waseda Univ.) IT2021-24 |
By considering the probability model with the structure that the data is divided into clusters and each cluster has an i... [more] | IT2021-24 pp.51-56 |
| IT | 2021-07-09 13:50 |
Online | Online | Projected gradient MIMO signal detection using Chebyshev step Asahi Mizukoshi, Tadashi Wadayama, Satoshi Takabe (NITech) IT2021-25 |
This paper proposes a projected gradient detection method using the Chebyshev steps for a signal detector in a MIMO (Mul... [more] | IT2021-25 pp.57-62 |
| DC | 2020-12-11 13:00 |
Hyogo | (Primary: On-site, Secondary: Online) |
A Degradation Prediction of Circuit Delay Using A Gradient Descent Method Seiichirou Mori, Masayuki Gondou, Yousuke Miyake, Takaaki Kato, Seiji Kajihara (Kyutech) DC2020-59 |
As the risk of aging-induced faults of VLSIs is increasing, highly reliable systems require to predict when the aging-in... [more] | DC2020-59 pp.1-6 |
| SIP | 2020-08-28 10:55 |
Online | Online | Theoretical Analysis on Convergence Acceleration of Deep-Unfolded Gradient Descent Satoshi Takabe, Tadashi Wadayama (NITech) SIP2020-35 |
Deep unfolding is a promising deep learning technique whose network architecture is based on existing iterative algorith... [more] | SIP2020-35 pp.25-30 |
| SP, EA, SIP | 2020-03-02 13:00 |
Okinawa | Okinawa Industry Support Center (Cancelled but technical report was issued) |
[Poster Presentation]
Restoration of clipped signal using oversampling based on differentiable and convex loss function Natsuki Ueno, Shoichi Koyama, Hiroshi Saruwatari (Univ. Tokyo) EA2019-126 SIP2019-128 SP2019-75 |
A signal reconstruction method of clipped time-continuous signal using oversampling is proposed. The signal reconstructi... [more] | EA2019-126 SIP2019-128 SP2019-75 pp.147-152 |
| NC, MBE | 2019-12-06 16:05 |
Aichi | Toyohashi Tech | Neural Networks for Constructing Logical Operations Yuma Saito, Masafumi Hagiwara (Keio Univ.) MBE2019-57 NC2019-48 |
In this research, we aim to integrate both conventional statistical processing and exact logic processing into the same ... [more] | MBE2019-57 NC2019-48 pp.73-78 |
| IBISML | 2017-03-07 11:30 |
Tokyo | Tokyo Institute of Technology | A stochastic optimization method and generalization bounds for voting classifiers by continuous density functions Atsushi Nitanda (Tokyo Tech./NTTDATA MSI), Taiji Suzuki (Tokyo Tech./JST/RIKEN) IBISML2016-108 |
We consider a learning method for the majority vote classifier by probability measure on continuously parametrized space... [more] | IBISML2016-108 pp.63-69 |
| NC, NLP (Joint) |
2017-01-27 13:25 |
Fukuoka | Kitakyushu Foundation for the Advanement of Ind. Sci. and Tech. | A study on multilayered neural network with simultaneous perturbation learning rule Kenji Onoue, Hidetaka Ito, Hiroomi Hikawa (Kansai Univ) NC2016-58 |
This paper describes a study on multilayered neural network with simultaneous perturbation learning rule. The learning ... [more] |
NC2016-58 pp.59-64 |
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