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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2020-10-21 09:45 |
Online | Online |  IBISML2020-18 |
A symbol emergence system is a multi-agent system where each autonomous agent forms internal representations through int... [more] | IBISML2020-18 pp.34-35 |
| PRMU | 2020-09-02 15:45 |
Online | Online | Collaborative learning for generative adversarial networks Takuya Tsukahara, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi (Chubu Univ.) PRMU2020-14 |
Generative adversarial networks (GANs) adversarially trains generative and discriminative models. And this is how to gen... [more] | PRMU2020-14 pp.41-46 |
| IT, EMM | 2020-05-28 15:25 |
Online | Online | An Autoregressive Image Generative Model and the Bayes Code for It Yuta Nakahara, Toshiyasu Matsushima (Waseda Univ.) IT2020-4 EMM2020-4 |
In this paper, we propose an autoregressive stochastic generative model for images. This model should be one of the mos... [more] |
IT2020-4 EMM2020-4 pp.19-24 |
| 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 |
|
| NLC, IPSJ-NL, SP, IPSJ-SLP (Joint) [detail] |
2019-12-06 10:35 |
Tokyo | NHK Science & Technology Research Labs. | [Invited Talk]
Progress and prospects of statistical speech synthesis Keiichi Tokuda (Nagoya Inst. of Tech.) SP2019-35 |
The basic problem of statistical speech synthesis is quite simple: we have a speech database for training, i.e., a set o... [more] | SP2019-35 pp.11-12 |
| IMQ, HIP | 2019-07-19 16:20 |
Hokkaido | Satellite Campus, Sapporo City University | A Note on Semantic Evaluation of Images Generated by Text-to-image Generative Adversarial Networks Rintaro Yanagi, Togo Ren, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) IMQ2019-5 HIP2019-33 |
Evaluating the quality of generated images from input sentences is important to verify the effectiveness of text-to-imag... [more] | IMQ2019-5 HIP2019-33 pp.21-24 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Realizing Large Scale Model by Integration of Stochastic Models
-- Implementation and Evaluation of Integrated Model of VAE, GMM, HMM and MLDA -- Ryo Kuniyasu, Tomoaki Nakamura, Tatsuya Aoki (UEC), Akira Taniguchi, Ryo Ozaki, Tomoro Ishimine (Ritsumeikan Univ.), Hiroki Yokoyama (Tamagawa Univ.), Tadashi Ogura (SOKENDAI), Takayuki Nagai (UEC), Tadahiro Taniguchi (Ritsumeikan Univ.) IBISML2018-77 |
In order to realize human-like intelligence artificially, large-scale cognitive models are required for robots to unders... [more] | IBISML2018-77 pp.249-254 |
| BioX, ITE-ME, ITE-IST | 2018-05-24 15:45 |
Ishikawa | Kanazawa Univ. Nishimachi Satelite Plaza | A probabilistic verification method using multi-feature for environment-robust biometrics Hidetsugu Uchida, Narishige Abe, Shigefumi Yamada (FUJITSU LAB.) BioX2018-3 |
This paper reports a probabilistic verification method using multi-feature for environment-robust biometrics. In biometri... [more] | BioX2018-3 pp.21-26 |
| DE, CEA, IPSJ-DBS | 2017-12-23 15:00 |
Tokyo | National Institute of Informatics | Learning Templates for Generalizing Procedural Texts Minari Yoshinari (Tohoku Univ.), Sho Yokoi, Kentaro Inui (Tohoku Univ./RIKEN) DE2017-36 |
In this research, we attempt to learn procedural templates from sets of procedures which achieve a common purpose. Our h... [more] | DE2017-36 pp.103-107 |
| PRMU | 2017-10-12 15:10 |
Kumamoto | [Tutorial Lecture]
Families of GANs Tomohiro Takahashi (ABEJA) PRMU2017-80 |
Generative Adversarial Networks(GANs) have recently gained popularity due to their ability to synthesize images which ar... [more] | PRMU2017-80 pp.95-100 |
|
| PRMU | 2016-10-20 16:10 |
Miyazaki | On the composition systems and their application to handwritten character recognition Akira Date, Hikari Kubota, Yusuke Yamada (Univ. Miyazaki) PRMU2016-94 |
Most of the pattern recognition methods currently used in the real world application are statistical ones, such as feedf... [more] | PRMU2016-94 pp.19-24 |
|
| NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] | 2016-07-06 10:25 |
Okinawa | Okinawa Institute of Science and Technology | A Semi-supervised Learning Method for Imbalanced Binary Classification Akinori Fujino, Naonori Ueda (NTT) IBISML2016-3 |
This paper presents a semi-supervised learning method for imbalanced binary classification where the number of positive ... [more] | IBISML2016-3 pp.195-200 |
| IBISML | 2015-03-06 10:30 |
Kyoto | Kyoto University | Quality control in human-machine hybrid crowdsourcing Toshihiro Watanabe (UTokyo), Toshinari Itoko, Shin Saito, Masatomo Kobayashi, Hironobu Takagi (IBM), Hisashi Kashima (Kyoto Univ.) IBISML2014-91 |
The power of crowdsourcing has dramatically shortened the required time to create accessible content for disabled people... [more] | IBISML2014-91 pp.47-54 |
| PRMU | 2011-02-17 10:30 |
Saitama | Automatic Image Annotation by Variational Random Forests Motofumi Fukui, Noriji Kato, Qi Wenyuan (Fuji Xerox) PRMU2010-209 |
Recently automatic image annotation (AIA) receives a lot of attention in the fields of information retrieval, and many i... [more] | PRMU2010-209 pp.7-12 |
|
| NLC, SP (Joint) [detail] |
2010-12-20 17:20 |
Tokyo | National Olympics Memorial Youth Center | Robust Acoustic Modeling Using MLLR Transformation-based Speech Feature Generation Arata Itoh, Sunao Hara, Norihide Kitaoka, Kazuya Takeda (Nagoya Univ.) NLC2010-19 SP2010-92 |
We propose a novel acoustic model training method based on the new acoustic feature generation. Recently, the speaker ad... [more] | NLC2010-19 SP2010-92 pp.55-60 |
| WBS, SAT (Joint) |
2010-06-11 10:10 |
Okinawa | Okinawa-ken-Seinen-Kaikan | A Study on Power Allocation for Non-regenerative OFDM Relay Systems Masato Saito (Univ. of the Ryukyus), Shuhei Haraguchi, Minoru Okada (NAIST) WBS2010-9 |
Non-regenerative OFDM (Orthogonal Frequency Division Multiplexing) relay systems can expand the range of wireless comm... [more] |
WBS2010-9 pp.47-52 |
| NC, MBE (Joint) |
2010-03-09 13:45 |
Tokyo | Tamagawa University | A Comparison of HHMMs and HHCRFs in State Sequence Estimation Hirotaka Tamada, Akira Hayashi, Nobuo Suematsu, Kazunori Iwata (Hiroshima City Univ.) NC2009-105 |
HMMs (hidden Markov models) are well-known generative models for time series data. Recently, however, CRFs (conditional ... [more] | NC2009-105 pp.101-106 |
| PRMU | 2008-12-19 10:00 |
Kumamoto | Kumamoto Univ. | Cascaded Traffic Sign Detector Using Generative Learning Considering Color Variance Keisuke Doman, Daisuke Deguchi (Nagoya Univ.), Tomokazu Takahashi (Gifu Shotoku Gakuen Univ.), Yoshito Mekada (Chukyo Univ.), Ichiro Ide, Hiroshi Murase (Nagoya Univ.) PRMU2008-170 |
A robust and real-time detection of traffic signs is important to support safe-driving. Viola et al. have proposed a ro... [more] |
PRMU2008-170 pp.135-140 |
| SP | 2008-03-20 15:15 |
Tokyo | Univ. Tokyo | [Poster Presentation]
A Context Clustering Technique for Improvement of Tone Intelligibility of Average-voice-based Thai Speech Synthesis Suphattharachai Chomphan, Takao Kobayashi (Tokyo Inst. of Tech.) SP2007-194 |
This paper describes a novel approach to the context clustering process in a speaker independent HMM-based Thai speech s... [more] | SP2007-194 pp.45-50 |
| NC | 2007-03-16 10:10 |
Tokyo | Tamagawa University | Learning nonlinear forward optics in generative models Satohiro Tajima, Masataka Watanabe (Tokyo Univ.) |
Visual processing is an inverse problem, say, the system needs to unravel the three dimensional representation of the wo... [more] | NC2006-188 pp.11-16 |
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