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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 236  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
SR, NS, SeMI, RCC, RCS
(Joint)
2020-07-09
15:30
Online Online [Invited Lecture] Consideration on Radio Resource Allocation Based on Optimization Algorithm
Nobuhiko Miki (Kagawa Univ.)
 [more]
MSS, NLP
(Joint)
2020-03-10
15:45
Aichi   Temporal Logic Falsification for Simulink models based on the hybrid robustness using ChainerRL
Ryota Owaki, Shoji Yuen (NU) MSS2019-67
We present a method of falsification for the hybrid property of Simulink model using deep reinforcement learning. This s... [more] MSS2019-67
pp.53-58
MSS, NLP
(Joint)
2020-03-10
16:45
Aichi   Reinforcement Learning Based Multi-Ship Course Search Method with Tracking Control
Hiroki Kimura, Takahiro Tomihara, Takeshi Kamio (Hiroshima City Univ.), Takahiro Tanaka (Japan Coast Guard Academy), Kunihiko Mitsubori (Takushoku Univ.), Hisato Fujisaka (Hiroshima City Univ.) NLP2019-131
We have developed multi-agent reinforcement learning system (MARLS) to search ships’ courses. Since the rudder angle is ... [more] NLP2019-131
pp.103-108
MSS, NLP
(Joint)
2020-03-10
17:10
Aichi   An Application of Deep Reinforcement Learning to Networked Control Under Communication Channel Constraints
Takahiro Kozuka, Kazumune Hashimoto, Toshimitsu Ushio (Osaka Univ.) NLP2019-132
In this report, we consider a networked control system under channel constrains. Our purpose is to optimize both control... [more] NLP2019-132
pp.109-113
IE, IMQ, MVE, CQ
(Joint) [detail]
2020-03-05
09:20
Fukuoka Kyushu Institute of Technology Self-Play Reinforcement Learning for Fast Image Retargeting
Nobukatsu Kajiura, Satoshi Kosugi, Xueting Wang, Toshihiko Yamasaki, Kiyoharu Aizawa (UTokyo) IMQ2019-40 IE2019-122 MVE2019-61
We address image retargeting, which is a task of adjusting input images into arbitrary sizes. In a previous method, they... [more] IMQ2019-40 IE2019-122 MVE2019-61
pp.127-131
NS, IN
(Joint)
2020-03-06
11:20
Okinawa Royal Hotel Okinawa Zanpa-Misaki Considering a human mobility model inspired by reinforcement learning
Yuutaro Iwai, Akihiro Fujihara (CIT) IN2019-118
Reinforcement learning is a machine learning framework that
an agent repeatedly observes reward from environment as a ... [more]
IN2019-118
pp.237-242
NC, MBE
(Joint)
2020-03-04
09:55
Tokyo University of Electro Communications Inverted pendulum control with redundancy by freezing model using deep reinforcement learning
Koki Hirakawa, Naohiro Fukumura (Toyohashi Univ. of Tech) NC2019-76
In recent years, various intelligent robots have been researched and developed, and multi-degree-of-freedom robots that ... [more] NC2019-76
pp.3-8
NC, MBE
(Joint)
2020-03-04
10:20
Tokyo University of Electro Communications The relationship between psychiatric diseases and less exploratory behaviors
Shuhei Hara (Waseda Univ), Kenji Doya (OIST), Takayuki Koga, Yuta Takahashi, Rieko Osu (Waseda Univ) NC2019-77
Less exploratory behaviors are seen in many psychiatric diseases. In our research, healthy subjects completed a behavior... [more] NC2019-77
pp.9-14
NC, MBE
(Joint)
2020-03-06
10:20
Tokyo University of Electro Communications Feature Extraction by Competitive Learning for Dynamic Q-Network
Taishi Komatsu, Yukari Yamauchi (NU) NC2019-106
Deep Q-Network is a reinforcement learning algorithm that performs feature extraction by convolution from state space in... [more] NC2019-106
pp.175-179
RCS, SR, SRW
(Joint)
2020-03-05
09:25
Tokyo Tokyo Institute of Technology Deep reinforcement learning based access control scheme for radio access networks
Hang Zhou, Xiaoyan Wang, Masahiro Umehira (Ibaraki Univ.) SR2019-123
After a disaster occurred, it is extremely important to reconstruct the network and provide the communication services t... [more] SR2019-123
pp.59-64
RCS, SR, SRW
(Joint)
2020-03-05
09:50
Tokyo Tokyo Institute of Technology Reinforcement learning based channel selection scheme for WiFi-LTE coexistence in unlicensed spectrum
Yuki Kishimoto, Xiaoyan Wang, Masahiro Umehira (Ibaraki Univ.) SR2019-124
In recent years, with the rapid increase in mobile traffic, there is an increasing demand of wider bandwidth for mobile ... [more] SR2019-124
pp.65-70
PN 2020-03-03
09:00
Kagoshima   Research of Scheduling Method Considering Traffic-Characteristics for Data Center Wide Area Network(DC-WAN)
Takashi Arakawa, Kohei Shiomoto (TCU), Takashi Kurimoto (NII) PN2019-61
Recent years, the demand for efficient use of Data-Center Wide Area Network (DC-WAN) is increasing.
Since delay affects... [more]
PN2019-61
pp.51-58
ITE-HI, IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] 2020-02-27
16:20
Hokkaido Hokkaido Univ. [Special Talk] Neighbor-Aware Approaches for Pixel Labeling
Ryosuke Furuta (TUS), Naoto Inoue, Toshihiko Yamasaki (UT) ITS2019-45 IE2019-83
Pixel labeling is one of the most classical and important problems in the field of computer vision because it has a vari... [more] ITS2019-45 IE2019-83
p.239
ITE-HI, IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] 2020-02-28
15:10
Hokkaido Hokkaido Univ. Unpaired Learning for Noise-free, Scale Invariant, and Interpretable Image Enhancement
Satoshi Kosugi, Toshihiko Yamasaki (Univ. of Tokyo) ITS2019-52 IE2019-90
This paper tackles unpaired image enhancement, a task of learning a mapping function which transforms input images into ... [more] ITS2019-52 IE2019-90
pp.311-316
SDM 2020-01-28
14:00
Tokyo Kikai-Shinko-Kaikan Bldg. [Invited Talk] Performance Maximization of In-Memory Reinforcement Learning with Variability-Controlled Hf1-xZrxO2 Ferroelectric Tunnel Junctions
Kensuke Ota, Marina Yamaguchi (kioxia), Radu Berdan, Takao Marukame, Yoshifumi Nishi (Toshiba), Kazuhiro Matsuo, Kota Takahashi, Yuta Kamiya, Shinji Miyano, Jun Deguchi, Shosuke Fujii, Masumi Saitoh (kioxia) SDM2019-84
We develop strategies to maximize the performance and reliability of in-memory reinforcement learning with Hf1-xZrxO2 fe... [more] SDM2019-84
p.9
HCS 2020-01-25
14:00
Oita Room407, J:COM HorutoHall OITA (Oita) the relationships between interpretation bias of indirect requests and learning rate parameters for social reward or punishment
Makoto Hirakawa (Hitoshima Univ.) HCS2019-61
People sometimes do not make requests directly. Instead, for example, the utterance “This room is cold” may attempt to c... [more] HCS2019-61
pp.41-45
HCS 2020-01-26
10:30
Oita Room407, J:COM HorutoHall OITA (Oita) Acquisition of Function Words That Represent Dialogue Acts -- Constructing a Hybrid Model of Automatic and Deliberate Processing --
Akane Matsushima, Natsuki Oka, Chie Fukada (Kyoto Institute of Technology), Yuko Yoshimura (Kanazawa Univ.), Koji Kawahara (Nagoya University of Foreign Studies) HCS2019-70
(To be available after the conference date) [more] HCS2019-70
pp.93-98
HCS 2020-01-26
13:40
Oita Room407, J:COM HorutoHall OITA (Oita) Unsupervised Double Articulation of Natural Speech in a Video Game Environment -- Toward a Constructive Understanding of Language Acquisition --
Kotaro Yamaguchi, Natsuki Oka (KIT), Tadahiro Taniguchi (Ritsumeikan Univ.) HCS2019-76
It is considered that infants have the ability to double articulation analysis (to segment into phonemes and words) unsu... [more] HCS2019-76
pp.129-134
MVE, IPSJ-CVIM 2020-01-23
15:50
Nara   [Invited Talk] Multimodal Information Processing and Intelligence -- Multimodal Categorization Revisited --
Takayuki Nagai (Osaka Univ.) MVE2019-31
(To be available after the conference date) [more] MVE2019-31
p.81
NLP, NC
(Joint)
2020-01-25
16:25
Okinawa Miyakojima Marine Terminal Reinforcement learning of communication strategy between players of the game of Contract Bridge
Yotaro Yamaguchi, Sotetsu Koyamada, Ken Nakae, Shin Ishii (Kyoto Univ.) NLP2019-111
Contract bridge (bridge) is a card game in which four players are divided into two teams and cooperate with a partner to... [more] NLP2019-111
pp.131-134
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