| IEICE Technical Committee Submission System Conference Schedule |
Online Proceedings [Sign in] Tech. Rep. Archives |
All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SANE | 2023-12-08 13:50 |
Overseas | Surakarta, Indonesia (Primary: On-site, Secondary: Online) |
Finger Position Detection Using Multitask Gaussian Process Regression on Noncontact Control Panels Takayuki Kitamura, Shingo Yamaura, Kengo Nishimoto, Tadashi Oshima (MELCO)  SANE2023-81 |
In recent years, the development of transparent antennas for fifth-generation mobile communication systems has progresse... [more] | SANE2023-81 pp.116-121 |
| DE, IPSJ-DBS, IPSJ-IFAT [detail] | 2023-09-21 15:00 |
Fukuoka | Kitakyushu International Conference Center | Analysis of subtasks for improving the detection accuracy of offensive tweets in multitask learning Ryoichi Sawada, Yu Suzuki (Gifu) DE2023-14 |
There are studies on detecting offensive tweets, but there is a need to further improve the accuracy.One method to impro... [more] | DE2023-14 pp.19-24 |
| HCS | 2019-08-23 16:15 |
Osaka | Jikei Institute | Estimating Exchange-level Annotations with Multitask Learning for Multimodal Dialogue Systems Yuki Hirano, Shogo Okada (JAIST), Haruto Nishimoto, Kazunori Komatani (Osaka Univ.) HCS2019-32 |
This study presents multimodal computational modeling for estimating three labels: user's interest label, user's sentim... [more] |
HCS2019-32 pp.15-20 |
| PRMU, IPSJ-CVIM | 2019-05-30 10:50 |
Tokyo | Estimating areas in images for grasping an object by a three-fingered robot hand Atsuki Tsukamoto, Ryosuke Kubota, Kiyoshi Kogure (KIT) PRMU2019-4 |
This paper proposes a method for estimation areas for grasping an object by a three-fingered robot hand. The method take... [more] | PRMU2019-4 pp.19-24 |
|
| AI | 2018-12-07 15:30 |
Fukuoka | Evolutionary Multitask Deep Reinforcement Learning in 2D Maze Task Shota Imai, Yuichi Sei, Yasuyuki Tahara, Akihiko Ohsuga (UEC) AI2018-29 |
In Deep reinforcement learning, it is difficult to converge when the exploration is insufficient or a reward is sparｓe. ... [more] | AI2018-29 pp.19-24 |
|
| PRMU, MI, IE, SIP | 2018-05-18 11:00 |
Gifu | Contour Extraction of Transparent Objects Using Fully Convolutional Networks Ryosuke Kubota, Kiyoshi Kogure (KIT) SIP2018-10 IE2018-10 PRMU2018-10 MI2018-10 |
In this paper, we propose two methods to extract contours of transparent objects from a grayscale image using fully conv... [more] | SIP2018-10 IE2018-10 PRMU2018-10 MI2018-10 pp.41-46 |
|
| NLC, IPSJ-IFAT | 2017-02-10 14:45 |
Osaka | NLC2016-50 |
These days, many people use several kinds of services and apps that are linked to social networking service (SNS). In th... [more] | NLC2016-50 pp.75-80 |
|
| IBISML | 2014-11-17 17:00 |
Aichi | Nagoya Univ. | [Poster Presentation]
Multitask learning meets tensor factorization: task imputation via convex optimization Kishan Wimalawarne (Tokyo Inst. of Tech.), Masashi Sugiyama (Univ. of Tokyo), Ryota Tomioka (TTIC) IBISML2014-49 |
We study a multitask learning problem in which each task is parametrized by a weight vector and indexed by a pair of ind... [more] | IBISML2014-49 pp.111-118 |
[Return to Top Page]