Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
COMP |
2021-03-08 13:30 |
Online |
Online |
[Invited Talk]
Sample-efficient Hamiltonian learning of quantum many-body systems Anurag Anshu (UC Berkley), Srinivasan Arunachalam (IBM), Tomotaka Kuwahara (RIKEN AIP), Mehdi Soleimanifar (MIT) COMP2020-32 |
[more] |
COMP2020-32 p.25 |
LOIS, ISEC, SITE |
2020-11-06 13:35 |
Online |
Online |
Report on the International Policy Trends in Data Protection
-- A Study on controller and processor in Europe 1 -- Naonori Kato (KDDI Research/RIKEN AIP), Masatomo Suzuki (Niigata Univ./RIKEN AIP), Yosuke Murakami (KDDI Research) ISEC2020-37 SITE2020-34 LOIS2020-17 |
[more] |
ISEC2020-37 SITE2020-34 LOIS2020-17 pp.31-36 |
IBISML |
2020-10-22 14:50 |
Online |
Online |
Suppressing explanations with irrelevant concepts in deep learning Munemasa Tomohiro (Tsukuba Univ), Fukuchi Kazuto, Akimoto Yohei, Sakuma Jun (Tsukuba Univ/Riken AIP) IBISML2020-32 |
TCAV [1], which is an explanation method using a concept that humans easily understand for deep learning models, concept... [more] |
IBISML2020-32 pp.61-68 |
SITE, ISEC, HWS, EMM, BioX, IPSJ-CSEC, IPSJ-SPT, ICSS [detail] |
2020-07-20 10:25 |
Online |
Online |
Report on the International Policy Trends in Data Protection
-- A Study on Facial Recognition Rules in Europe -- Naonori Kato (KDDI Research Inc./RIKEN AIP), Masatomo Suzuki (Niigata Univ./RIKEN AIP), Yosuke Murakami (KDDI Research Inc.) ISEC2020-15 SITE2020-12 BioX2020-18 HWS2020-8 ICSS2020-2 EMM2020-12 |
[more] |
ISEC2020-15 SITE2020-12 BioX2020-18 HWS2020-8 ICSS2020-2 EMM2020-12 pp.9-13 |
SITE, IPSJ-EIP [detail] |
2020-06-04 11:20 |
Online |
Online |
A Study on Digital Policy in Europe
-- Comparison with Digital Single Market Strategy -- Naonori Kato (KDDI Research Inc./RIKEN AIP), Masatomo Suzuki (Niigata Univ./RIKEN AIP), Yosuke Murakami (KDDI Research Inc.) SITE2020-3 |
[more] |
SITE2020-3 pp.13-19 |
SITE, IPSJ-EIP [detail] |
2020-06-04 13:35 |
Online |
Online |
[Invited Talk]
AI Ethics: Personal AI Agent and its Applications Hiroshi Nakagawa (RIKEN AIP) SITE2020-4 |
[more] |
SITE2020-4 pp.21-28 |
SITE, IPSJ-EIP [detail] |
2020-06-04 14:00 |
Online |
Online |
[Invited Talk]
Trends in international AI ethics policies Naonori Kato (KDDI Research/RIKEN AIP) SITE2020-5 |
[more] |
SITE2020-5 pp.29-34 |
IBISML |
2020-03-11 14:35 |
Kyoto |
Kyoto University (Cancelled but technical report was issued) |
IBISML2019-47 |
[more] |
IBISML2019-47 pp.95-100 |
COMP |
2019-12-13 17:00 |
Gunma |
Ikaho Seminar House, Gunma University |
COMP2019-42 |
Motivated by adjacency in perfect matching polytopes, we study the shortest reconfiguration problem of perfect matchings... [more] |
COMP2019-42 pp.93-100 |
NLC, IPSJ-DC |
2019-09-27 17:50 |
Tokyo |
Future Corporation |
Improvement of Comment Ranking in Yahoo! News by In-House Competition Hiroaki Taguchi (Yahoo Japan), Soichiro Fujita (Tokyo Tech), Hayato Kobayashi (Yahoo Japan/RIKEN), Yoshimune Tabuchi, Ken Kobayashi (Yahoo Japan), Kazuma Murao (VISITS Technologies), Chahine Koleejan, Takeshi Masuyama, Taichi Yatsuka (Yahoo Japan), Manabu Okumura (Tokyo Tech) NLC2019-16 |
In this paper, we report on efforts of an in-house competition held for improving models that rank user comments in Yaho... [more] |
NLC2019-16 pp.41-46 |
SP, IPSJ-SLP (Joint) |
2019-07-20 11:15 |
Niigata |
FURINYA(Tsukioka-Onsen, Niigata) |
[Invited Talk]
Towards Multimodal Machine Speech Chain Sakriani Sakti, Satoshi Nakamura (NAIST/RIKEN AIP) SP2019-7 |
[more] |
SP2019-7 pp.3-7 |
COMP |
2019-03-18 11:45 |
Tokyo |
The University of Tokyo |
[Invited Talk]
The Diameter of Dense Random Regular Graphs Nobutaka Shimizu (Univ. Tokyo/RIKEN AIP) COMP2018-48 |
For fixed $n$ and $d$, we consider a graph with minimum diameter among
all $d$-regular graphs on $n$ vertices.
This p... [more] |
COMP2018-48 p.41 |
SC |
2019-03-15 16:45 |
Tokyo |
National Institute of Informatics |
Developing Smart Entry-Exit Service for Office Using Face Identification Sensor Box and Virtual Agent Kosuke Hirayama, Sachio Saiki (Kobe Univ.), Masahide Nakamura (Kobe Univ./Riken AIP) SC2018-41 |
To follow that many applications start to incorporate face identifying function, we have developed ``Face identification... [more] |
SC2018-41 pp.25-30 |
IBISML |
2019-03-06 11:00 |
Tokyo |
RIKEN AIP |
Shapelet-based Multiple-Instance Learning Daiki Suehiro, Kohei Hatano (Kyushu Univ./RIKEN AIP), Eiji Takimoto (Kyushu Univ.), Shuji Yamamoto, Kenichi Bannai (Keio Univ./RIKEN AIP), Akiko Takeda (The Univ. of Tokyo/RIKEN AIP) IBISML2018-112 |
[more] |
IBISML2018-112 pp.51-58 |
AI |
2019-02-23 14:40 |
Tokyo |
|
Estimation of Tags using Various Data for Online Video Sharing Sites Hiroki Sakaji (U-Tokyo), Akio Kobayashi (AIP), Masaki Kohana (Ibaraki Univ.), Yasunao Takano (Aoyama Gakuin Univ.), Kiyoshi Izumi (U-Tokyo) AI2018-50 |
[more] |
AI2018-50 pp.71-75 |
NLC, IPSJ-NL, SP, IPSJ-SLP (Joint) [detail] |
2018-12-12 17:00 |
Tokyo |
Waseda Univ. Nishiwaseda Campus |
Application of Neural Title Generation as News Editing Assistant Kazuma Murao (Yahoo Japan Corporation), Hayato Kobayashi (Yahoo Japan Corporation/RIKEN AIP), Taichi Yatsuka, Ken Kobayashi, Takeshi Masuyama, Tatsuru Higurashi, Yoshimune Tabuchi (Yahoo Japan Corporation) NLC2018-34 |
There have been many studies on automatic title generation based on neural networks.
However, there are a number of cha... [more] |
NLC2018-34 pp.109-115 |
IBISML |
2018-11-05 15:10 |
Hokkaido |
Hokkaido Citizens Activites Center (Kaderu 2.7) |
[Poster Presentation]
Watermarking of Neural Network with Exponential Weighting Parameters Ryota Namba (Tsukuba Univ.), Jun Sakuma (Tsukuba Univ./riken) IBISML2018-63 |
Deep learning has been achieving top performance in many tasks.
Since training of a deep learning model requires a gr... [more] |
IBISML2018-63 pp.143-150 |
IBISML |
2017-11-09 13:00 |
Tokyo |
Univ. of Tokyo |
IBISML2017-48 |
[more] |
IBISML2017-48 pp.101-107 |
IBISML |
2017-11-09 13:00 |
Tokyo |
Univ. of Tokyo |
Consequently Fair Contextual Bandit Learning Kazuto Fukuchi (Univ. of Tsukuba), Jun Sakuma (Univ. of Tsukuba/JST/RIKEN) IBISML2017-53 |
Fairness in machine learning is being recognized as an important field. It requires that the consequent decisions made b... [more] |
IBISML2017-53 pp.139-146 |