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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| RCS, SIP, IT | 2025-01-30 13:10 |
Yamaguchi | Kaikyou-messe (Yamaguchi, Online) (Primary: On-site, Secondary: Online) |
Particle-based Variational Inference using Local Entropy for Multimodal Distributions Yuya Kawamura, Satoshi Takabe (Science Tokyo)  IT2024-67 SIP2024-106 RCS2024-250 |
(To be available after the conference date) [more] | IT2024-67 SIP2024-106 RCS2024-250 pp.238-243 |
| SIP | 2024-08-27 13:55 |
Fukui | University of Fukui (Bunkyo Campus) (Fukui, Online) (Primary: On-site, Secondary: Online) |
Transfer Learning for Deep-Unfolded Combinatorial Optimization Solver with Quantum Annealer Ryo Hagiwara, Shunta Arai, Satoshi Takabe (Tokyo Tech) SIP2024-59 |
This paper proposes transfer learning for a trainable sampling-based COP solver applying deep learning technique called ... [more] | SIP2024-59 pp.69-74 |
| MSS, CAS, SIP, VLD | 2023-07-06 14:40 |
Hokkaido | (Hokkaido, Online) (Primary: On-site, Secondary: Online) |
Convergence Acceleration of Particle-based Variational Inference by Deep Unfolding Yuya Kawamura, Satoshi Takabe (Tokyo Tech) CAS2023-8 VLD2023-8 SIP2023-24 MSS2023-8 |
Stein Variational Gradient Descent(SVGD) is a prominent particle-based variational inference method used for estimating ... [more] | CAS2023-8 VLD2023-8 SIP2023-24 MSS2023-8 pp.37-42 |
| RCS, SIP, IT | 2022-01-21 11:20 |
Online | 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 13:50 |
Online | 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 |
| IT | 2021-07-09 14:30 |
Online | Online (Online) | Construction of Dimension Reduction Matrix for Signal Recovery of Multivariate Gaussian Vectors Kento Yokoyama, Tadashi Wadayama, Satoshi Takabe (NIT) IT2021-26 |
In compressed sensing, we discuss the problem of estimating the sparse original signal $¥bm{x} ¥in ¥mathbb{R}^n$ from th... [more] | IT2021-26 pp.63-68 |
| EMM, IT | 2021-05-21 10:40 |
Online | Online (Online) | Proximal Decoding for LDPC-coded Massive MIMO Channels Tadashi Wadayama, Satoshi Takabea (Nitech) IT2021-9 EMM2021-9 |
[more] | IT2021-9 EMM2021-9 pp.48-53 |
| SIP, IT, RCS | 2021-01-22 14:25 |
Online | Online (Online) | Accelerating Fixed-point Iteration with Deep Unfolded-Periodical Successive Over Relaxation Yuan Qi, Wadayama Tadashi, Satoshi Takabe (NiTech) IT2020-106 SIP2020-84 RCS2020-197 |
[more] | IT2020-106 SIP2020-84 RCS2020-197 pp.241-246 |
| IT | 2020-09-04 10:05 |
Online | Online (Online) | A Study on Complex-valued Sparse CDMA Detection Using Deep Learning Technique Yuki Yamauchi, Satoshi Takabe, Tadashi Wadayama (NITech) IT2020-20 |
[more] | IT2020-20 pp.13-18 |
| IT | 2020-09-04 10:45 |
Online | Online (Online) | Chebyshev Periodical SOR Methods for Accelerating Fixed-Point Iterations Tadashi Wadayama, Satoshi Takabe (NiTech) IT2020-21 |
[more] | IT2020-21 pp.19-24 |
| SIP | 2020-08-27 13:30 |
Online | Online (Online) | [Invited Talk]
Recent Advance of Deep-Unfolded Algorithms for Signal Processing and Wireless Communications Satoshi Takabe (NITech) SIP2020-30 |
In this talk, I will briefly review recent progress of deep unfolding as a promising deep learning technique. A network ... [more] | SIP2020-30 p.11 |
| SIP | 2020-08-28 10:55 |
Online | 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 |
| IT | 2020-07-16 14:20 |
Online | Online (Online) | A Study on Trainable ISTA using Auto Encoder as Shrinkage Function for Image Recovery Kento Yokoyama, Satoshi Takabe, Tadashi Wadayama (NIT) IT2020-13 |
ISTA (Iterative Shrinkage-Thresholding Algorithm) is one of the basic algorithms used in compressed sensing to estimate ... [more] | IT2020-13 pp.13-18 |
| SR, NS, SeMI, RCC, RCS (Joint) |
2020-07-08 13:50 |
Online | Online (Online) | Deep Unfolded Multicast Beamforming for Massive MIMO Satoshi Takabe, Tadashi Wadayama (NITech) RCS2020-64 |
Multicast beamforming is a promising technique for multicast communications to design a beamforming vector based on chan... [more] | RCS2020-64 pp.37-42 |
| RCS, SR, SRW (Joint) |
2020-03-05 10:30 |
Tokyo | Tokyo Institute of Technology (Tokyo) (Cancelled but technical report was issued) |
[Invited Lecture]
Recent Advance of Deep Unfolding-based Algorithms for Wireless Communications Satoshi Takabe (NITech) SR2019-125 |
In this paper, we briefly review recent progress of deep unfolding that is a promising deep learning technique whose net... [more] | SR2019-125 pp.71-78 |
| IT | 2019-07-26 10:55 |
Tokyo | NATULUCK-Iidabashi-Higashiguchi Ekimaeten (Tokyo) | Complex-field TISTA for Nonlinear Inverse Problems Satoshi Takabe, Tadashi Wadayama (NITech) IT2019-23 |
[more] | IT2019-23 pp.43-48 |
| RCS, SIP, IT | 2019-01-31 12:30 |
Osaka | Osaka University (Osaka) | Trainable ISTA
-- Deep learning-based iterative algorithm for sparse signal recovery -- Satoshi Takabe, Tadashi Wadayama (NITech) IT2018-45 SIP2018-75 RCS2018-252 |
(To be available after the conference date) [more] | IT2018-45 SIP2018-75 RCS2018-252 pp.61-66 |
| IT, EMM | 2018-05-17 14:00 |
Tokyo | Ookayama Campus, Tokyo Institute of Technology (Tokyo) | Asymptotic Analysis of Spatially-Coupled LDPC Codes for Two-Way Relay Channels Satoshi Takabe, Tadashi Wadayama (NITech), Masahito Hayashi (Nagoya Univ.) IT2018-1 EMM2018-1 |
Compute-and-forward relaying (CAF) is effective to increase bandwidth efficiency of wireless two-way relay channels. In ... [more] | IT2018-1 EMM2018-1 pp.1-6 |
| WBS, IT, ISEC | 2018-03-09 11:40 |
Tokyo | Katsusika Campas, Tokyo University of Science (Tokyo) | Asymptotic Analysis of LDPC Codes for Two-Way Relay Channels Yuta Ishimatsu, Satoshi Takabe, Tadashi Wadayama (NITech), Masahito Hayashi (Nagoya Univ.) IT2017-131 ISEC2017-119 WBS2017-112 |
Compute-and-forward relaying is a relaying scheme to improve bandwidth efficiency of wireless two-way relay channels. R... [more] |
IT2017-131 ISEC2017-119 WBS2017-112 pp.167-172 |
| IT | 2017-07-13 15:45 |
Chiba | Chiba University (Chiba) | Phase Transition in Network Connectivity Robustness against Stochastic Node Removal Satoshi Takabe, Takafumi Nakano, Tadashi Wadayama (NITech) IT2017-25 |
Recently, a reliability analysis on network connectivity against stochastic node removal is introduced by Nozaki et al. ... [more] | IT2017-25 pp.49-54 |
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