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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2022-09-15 15:05 |
Kanagawa | Keio Univ. (Yagami Campus) (Primary: On-site, Secondary: Online) |
Improving Efficiency of Regularization Path Computation in Safe Pattern Pruning via Multiple Referential Solutions Takumi Yoshida (Nitech), Hiroyuki Hanada (RIKEN), Kazuya Nakagawa, Shinya Suzumura, Onur Boyar, Kazuki Iwata (Nitech), Shun Shimura, Yuji Tanaka (NaogyaU), Masayuki Karasuyama (Nitech), Kouichi Taji (NaogyaU), Koji Tsuda (UTokyo/RIKEN), Ichiro Takeuchi (NaogyaU/RIKEN)  IBISML2022-38 |
Safe Screening and Safe Pattern Pruning are methods for efficiently modeling high-dimensional features by $L_1$-regulari... [more] | IBISML2022-38 pp.39-46 |
| PN | 2022-08-29 10:20 |
Hokkaido | (Primary: On-site, Secondary: Online) |
Compensation Performance of DNN-based Nonlinear Equalizer for Optical Communication Systems Jinya Nakamura, Kai Ikuta, Daisuke Motai, Moriya Nakamura (Meiji Univ.) PN2022-10 |
We investigated and compared the performances of three-layer-ANN- and four-layer-DNN-based nonlinear equalizers used for... [more] | PN2022-10 pp.10-14 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2022-06-27 17:25 |
Okinawa | (Primary: On-site, Secondary: Online) |
Additive Cumulative Link Model with Total Variation Regularization Hiroya Iyori, Shin Matsushima (Univ. of Tokyo) NC2022-8 IBISML2022-8 |
In many fields such as medical research and social science, data on an ordinal scale are often obtained. Problems in wh... [more] |
NC2022-8 IBISML2022-8 pp.69-75 |
| ICM | 2022-03-04 09:00 |
Online | Online | Proposal and Evaluation of a Query Representation and Retrieval Method for Operation Logs Hidetaka Koya, Akira Kataoka, Haruo Oishi (NTT) ICM2021-47 |
In recent years, various studies have been conducted using detailed operation histories (operation logs) for software GU... [more] | ICM2021-47 pp.29-34 |
| MBE, NC (Joint) |
2022-03-02 11:00 |
Online | Online | Learning of a stacked autoencoder with regularizers added to the cost function, evaluation of their effectiveness, and clarification of its information compression mechanism Masumi Ishikawa (Kyutech) NC2021-49 |
Deep learning has a serious drawback in that the resulting models tend to be a black box, hence hard to understand. A sp... [more] | NC2021-49 pp.17-22 |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-21 16:15 |
Online | Online | A Note on Realizing Adversarial Defense Based on Regularization of Multi-stage Squeeze-and-Excitation Features Jiahuan Zhang, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
Regularizing deep features is a common adversarial defense method. However, the existing methods do not further explore ... [more] | |
| LOIS, ICM | 2022-01-28 10:00 |
Online | Online | An Objective Article Search Method from Printed Japanese Contract Document Using Optical Character Recognition Shixi Chen, Masaki Sakagami, Nobuo Funabiki (Okayama Univ.), Takashi Toshida, Kohei Suga (Astrolab) ICM2021-39 LOIS2021-37 |
A contract is essential for the involved companies to have successful businesses among them. Then, the contract document... [more] | ICM2021-39 LOIS2021-37 pp.34-39 |
| NLP, MICT, MBE, NC (Joint) [detail] |
2022-01-23 12:10 |
Online | Online | Deep learning of mixture of continuous and categorical data with regularizers added to the cost function and evaluation of the effectiveness of sparse modeling Masumi Ishikawa (Kyutech) NC2021-45 |
Deep learning has a serious drawback in that the resulting models tend to be a black box, hence hard to understand. A sp... [more] | NC2021-45 pp.65-70 |
| PRMU | 2021-12-17 15:15 |
Online | Online | Task-independent redundancy reduction method using regularization for efficient neural network training Charvi Vitthal, Florian Beye, Koichi Nihei, Hayato Itsumi (NEC) PRMU2021-58 |
Neural networks (NNs) are widely used for various applications in recent years. However, it is difficult for the NN to l... [more] | PRMU2021-58 pp.188-194 |
| IN, IA (Joint) |
2021-12-17 12:40 |
Hiroshima | Higashi-Senda campus, Hiroshima Univ. (Primary: On-site, Secondary: Online) |
[Short Paper]
A Study on First Passage Time and Cover Time of Non-backtracking Random Walk on a Graph Tetsuya Kawagishi, Jo Hagikura, Ryotaro Matsuo, Hiroyuki Ohsaki (Kwansei Gakuin Univ.) IA2021-41 |
Recently, mathematical properties of random walks on a graph have been studied. Also, several extended models of a simp... [more] |
IA2021-41 pp.56-59 |
| RISING (3rd) |
2021-11-17 11:00 |
Tokyo | (Primary: On-site, Secondary: Online) |
An Overloaded IoT Signal Detection Method Using Piecewise Continuous Nonconvex Sparse Regularizer Atsuya Hirayama (Osaka City Univ.), Kazunori Hayashi (Kyoto Univ.) |
In this talk, we consider the signal detection problem of overloaded massive multi-user multi-input multi-output (MU-MIM... [more] | |
| SIP | 2021-08-23 14:00 |
Online | Online | [Invited Talk]
Block-Sparse Estimation using Optimal Block Structure Hiroki Kuroda (Ritsumeikan Univ.) SIP2021-29 |
This talk presents a convex optimization based block-sparse estimation method which is effective even when concrete bloc... [more] | SIP2021-29 p.11 |
| SP, IPSJ-SLP, IPSJ-MUS | 2021-06-18 13:00 |
Online | Online | F0 estimation of speech based on l2-norm regularized TV-CAR analysis Keiichi Funaki (Univ. of the Ryukyus) SP2021-2 |
Linear Prediction (LP) is the most successful speech analysis in speech processing, including speech coding implemented ... [more] |
SP2021-2 pp.7-12 |
| IE, SIP, BioX, ITE-IST, ITE-ME [detail] | 2021-06-03 17:00 |
Online | Online | Quadratic Surface Clustering Based on Hyperslab Projection and Its Application to Color Artifact Removal Tatsuya Ohtsubo, Seisuke Kyochi (The Univ. of Kitakyushu) SIP2021-5 BioX2021-5 IE2021-5 |
Color artifact removal based on local color nuclear norm (LCNN) has been proposed conventionally. LCNN relies on the fac... [more] | SIP2021-5 BioX2021-5 IE2021-5 pp.21-26 |
| COMP | 2021-03-08 16:30 |
Online | Online | On the Existence of 4-regular Uniquely Hamiltonian Graphs Ryota Sakamoto (UEC Tokyo) COMP2020-36 |
In 1946, Smith showed that a Hamiltonian cubic graph contains at least three Hamiltonian cycles. Then, a lot of research... [more] | COMP2020-36 pp.46-50 |
| WBS, IT, ISEC | 2021-03-04 09:25 |
Online | Online | List-Pruning SCL Decoder for Polar Codes Using Parity-Check Bits Yusuke Oki, Ryo Shibata, Hiroyuki Yashima (TUS) IT2020-112 ISEC2020-42 WBS2020-31 |
In this paper, we propose encoding and decoding algorithms of polar codes which add pruning bits into the transmitted in... [more] | IT2020-112 ISEC2020-42 WBS2020-31 pp.1-6 |
| LOIS | 2021-03-04 13:25 |
Online | Online | A Study of Product Identification System Using Optical Character Recognition Shixi Chen, Nobuo Funabiki, Masaki Sakagami (Okayama Univ.), Takashi Toshida, Kohei Suga (Astrolab) LOIS2020-48 |
Recently, the optical character recognition (OCR) technology has been remarkably progressed due to the advancements of d... [more] | LOIS2020-48 pp.6-11 |
| DC, SS | 2020-10-19 13:25 |
Online | Online | LTL Model Checking for Register Pushdown Systems Ryoma Senda (Nagoya Univ.), Yoshiaki Takata (KUT), Hiroyuki Seki (Nagoya Univ.) SS2020-6 DC2020-23 |
A pushdown system (PDS) is known as an abstract model of recursive programs. For PDS, model checking methods have been ... [more] |
SS2020-6 DC2020-23 pp.7-12 |
| SIS, ITE-BCT | 2020-10-01 13:40 |
Online | Online | Image Regularization with Morphological Gradient Priors Using Optimization of Multiple Structuring Elements for Each Pixel Hirotaka Oka, Mistuji Muneyasu, Soh Yoshida (Kansai Univ.), Makoto Nakashizuka (CIT) SIS2020-15 |
In image regularization, a method for restoring an image has been proposed in which a morphological gradient is used as ... [more] | SIS2020-15 pp.29-34 |
| SIP | 2020-08-28 13:30 |
Online | Online | [Invited Talk]
Image smoothing based on L0 gradient regularization and its applications Ryo Matsuoka (Univ. of Kitakyushu) SIP2020-37 |
This talk outlines research on image processing based on L0 gradient regularization that promotes sparseness in the grad... [more] | SIP2020-37 p.33 |
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