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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 13 of 13  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
IBISML 2023-12-21
15:25
Tokyo National Institute of Informatics
(Primary: On-site, Secondary: Online)
A linear time approximation of Wasserstein distance with word embedding selection
Sho Otao (Kyoto Univ.), Makoto Yamada (OIST) IBISML2023-38
Wasserstein distance, which can be computed by solving the optimal transport problem, is a powerful method for measuring... [more] IBISML2023-38
pp.50-57
EE, WPT
(Joint)
2023-10-05
13:50
Osaka
(Primary: On-site, Secondary: Online)
[Invited Talk] Optimal Locaion Model of In-motion Wireless Power Transfer System Based on Discrete Optimization -- Numerical Examples in Urban-scale and Regional-scale Regions --
Yudai Honma, Daisuke Hasegawa, Katsuhiro Hata, Takashi Oguchi (Univ. of Tokyo) WPT2023-20
The popularization of electric vehicles (EVs) is limited by their driving range and long charging times. To address this... [more] WPT2023-20
pp.2-7
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] 2023-06-29
16:50
Okinawa OIST Conference Center
(Primary: On-site, Secondary: Online)
Fast Regularized Discrete Optimal Transport with Group-Sparse Regularizers
Yasutoshi Ida, Sekitoshi Kanai, Kazuki Adachi, Atsutoshi Kumagai, Yasuhiro Fujiwara (NTT) NC2023-8 IBISML2023-8
When we use discrete optimal transport (OT) for unsupervised domain adaptation, a group-sparse regularizer is frequently... [more] NC2023-8 IBISML2023-8
pp.48-55
NC, MBE
(Joint)
2023-03-14
09:55
Tokyo The Univ. of Electro-Communications
(Primary: On-site, Secondary: Online)
An extension of the Sinkhorn divergence via convex combination of the bias correction term
Kyohei Miwa, Yu Nishiyama (UEC) NC2022-100
Sinkhorn divergence (SD) is a divergence in which a bias correction term is added to the entropic optimal transport (EOT... [more] NC2022-100
pp.48-53
IBISML 2022-12-23
15:10
Kyoto Kyoto University
(Primary: On-site, Secondary: Online)
Fixed Support Tree-Sliced Wasserstein Barycenter
Yuki Takezawa, Ryoma Sato (Kyoto Univ./RIKEN AIP), Zornitsa Kozareva (Meta AI), Sujith Ravi (SliceX AI), Makoto Yamada (Kyoto University/RIKEN AIP/OIST) IBISML2022-63
The Wasserstein barycenter has been widely studied in various fields, including natural language processing, and compute... [more] IBISML2022-63
pp.142-149
ISEC, SITE, LOIS 2022-11-18
14:30
Online Online Investigating the Use of Latent Representations in Federated Learning Using Normalizing Flow and Optimal Transport Algorithms
Motonobu Uchikoshi, Takeshi Mori (JRI) ISEC2022-35 SITE2022-39 LOIS2022-19
(To be available after the conference date) [more] ISEC2022-35 SITE2022-39 LOIS2022-19
pp.27-31
IBISML 2022-03-08
10:25
Online Online Robust computation of optimal transport by β-potential regularization
Shintaro Nakamura (Univ. Tokyo), Han Bao (Univ.Tokyo/RIKEN), Masashi Sugiyama (RIKEN/Univ. Tokyo) IBISML2021-31
Optimal transport (OT) has become a widely used tool to measure the discrepancy between probability distributions
in th... [more]
IBISML2021-31
pp.8-14
IBISML 2022-01-18
14:40
Online Online Domain Adaptation with Optimal Transport for Extended Variable Space
Toshimitsu Atiake (ISM), Hideitsu Hino (ISM/RIKEN) IBISML2021-27
Domain adaptation aims to transfer knowledge of labeled instances obtained from a source domain to a target domain to fi... [more] IBISML2021-27
pp.67-74
IBISML 2020-10-21
15:35
Online Online IBISML2020-24 The goal of this study is to understand the information processing mechanism in a deep neural network (DNN) as a curve $... [more] IBISML2020-24
p.42
NLP, NC
(Joint)
2020-01-24
10:10
Okinawa Miyakojima Marine Terminal Optimal Transport based Autoencoder for class and style Disentanglement
Florian Tambon, Tetsuo Furukawa (Kyutech) NC2019-62
The Sinkhorn autoencoder is a novel generative model using optimal transport to model the aggregated posterior from samp... [more] NC2019-62
pp.17-22
IBISML 2018-11-05
15:10
Hokkaido Hokkaido Citizens Activites Center (Kaderu 2.7) [Poster Presentation] How does the complexity of critic affect the performance of WGAN?
Akihiro Iohara, Toshiyuki Tanaka (Kyoto Univ.) IBISML2018-60
WGAN is a generative model that learns by minimizing the Wasserstein distance between the generator distribution and the... [more] IBISML2018-60
pp.119-125
NLP 2014-01-22
14:30
Hokkaido Niseko Park Hotel Control of Vehicles by Updating Optimal Velocity Function based on Local Information
Yuta Minoshima, Hiroya Nakao (Tokyo Inst. of Tech.) NLP2013-157
Traffic congestion of vehicles is considered to be a social problem, leading to waste of transportation time. To suppre... [more] NLP2013-157
pp.145-149
MoNA, IPSJ-AVM 2011-07-14
13:20
Nara Nara Institute of Science and Technology SCTP Optimum Path Selection using Ant Colony Optimization Approach and Heartbeat Chunks
Muhammad Ariff Bin Baharudin, Eiji Kamioka (Shibaura Inst. of Tech.) MoMuC2011-13
This paper proposes an optimum path selection approach by using the Ant Colony Optimization (ACO) technique and HEARTBEA... [more] MoMuC2011-13
pp.1-6
 Results 1 - 13 of 13  /   
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