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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 23  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
PRMU, IBISML, IPSJ-CVIM [detail] 2023-03-02
Hokkaido Future University Hakodate
(Primary: On-site, Secondary: Online)
Novel Adversarial Attacks Based on Embedding Geometry of Data Manifolds
Masahiro Morita, Hajime Tasaki, Jinhui Chao (Chuo Univ.) PRMU2022-84 IBISML2022-91
It has been shown recently that adversarial examples inducing misclassification by deep neural networks exist in the ort... [more] PRMU2022-84 IBISML2022-91
SP, IPSJ-SLP, EA, SIP [detail] 2023-03-01
(Primary: On-site, Secondary: Online)
Multiscale Manifold Clustering and Embedding with Multiple Kernels
Kyohei Suzuki, Masahiro Yukawa (Keio Univ.) EA2022-123 SIP2022-167 SP2022-87
This paper presents a clustering and embedding method to analyze data which lie on a union of multiple manifolds having ... [more] EA2022-123 SIP2022-167 SP2022-87
SIP 2022-08-26
Okinawa Nobumoto Ohama Memorial Hall (Ishigaki Island)
(Primary: On-site, Secondary: Online)
Design of Structured Convolutional Dictionary by Manifold Optimization for Image Restoration
Soushi Takahashi, Shogo Muramatsu (Niigata Univ.) SIP2022-76
This work reports on the effectiveness of a structured convolutional dictionary learning that introduces manifold optimi... [more] SIP2022-76
EA, SIP, SP, IPSJ-SLP [detail] 2022-03-02
(Primary: On-site, Secondary: Online)
[Poster Presentation] Epileptic Seizure Detection Using Active Learning with Riemannian Manifold
Toshiki Orihara, Toshihisa Tanaka (TUAT) EA2021-96 SIP2021-123 SP2021-81
In order to realize machine learning for diagnosis, it is necessary to solve the problem that the training model is not ... [more] EA2021-96 SIP2021-123 SP2021-81
PRMU 2021-10-09
Online Online Explaining Adversarial Examples by the Embedding Structure of Data Manifold
Hajime Tasaki, Yuji Kaneko, Jinhui Chao (Chuo Univ.) PRMU2021-19
It is widely known that adversarial examples cause misclassification in classifiers using deep learning. Inspite of nume... [more] PRMU2021-19
PRMU, MI, IPSJ-CVIM [detail] 2019-09-05
Okayama   Analysis and Feature Selection of CNN Features -- Recognition of Neoplasia by using Endocytoscopic Images --
Hayato Itoh (Nagoya Univ.), Yuichi Mori, Masashi Misawa (Showa Univ.), Masahiro Oda (Nagoya Univ.), Shin-Ei Kudo (Showa Univ.), Kensaku Mori (Nagoya Univ.) PRMU2019-29 MI2019-48
Pathological pattern classification is based on texture patterns in ultra magnified view of polyp surfaces.
Deep learni... [more]
PRMU2019-29 MI2019-48
MI 2019-01-23
Okinawa   Feature Selection from Imbalanced Data -- Pathological Pattern Classification in Endocytoscopic Images --
Hayato Itoh (Nagoya Univ.), Yuichi Mori, Masashi Misawa (Showa Univ.), Masahiro Oda (Nagoya Univ.), Shin-Ei Kudo (Showa Univ.), Kensaku Mori (Nagoya Univ.) MI2018-87
Endocytoscope gives ultramagnified observation that enables physicians to achieve minimally invasive and real-time diagn... [more] MI2018-87
(Joint) [detail]
Okinawa   Feature-selection method based on Grassmann distance for the classification of neoplastic polyps on endocytoscopic images
Hayato Itoh (Nagoya Univ.), Yuichi Mori, Masashi Misawa (Showa Univ.), Masahiro Oda (Nagoya Univ.), Shin-ei Kudo (Showa Univ.), Kensaku Mori (Nagoya Univ.) MI2017-81
An endocytoscope provides ultramagnified observation that enable physicians to achieve minimally invasive and real-time ... [more] MI2017-81
Miyagi Tohoku University NC2017-32 Continuous latent variable model is a category of dimension reduction methods, which estimates low dimensional latent va... [more] NC2017-32
PRMU, BioX 2017-03-20
Aichi   Structure Estimation of Topological Manifolds and Manifold Learning
Hajime Tasaki (Chuo Univ.), Reiner Lenz (Linkoping Univ.), Jinhui Chao (Chuo Univ.) BioX2016-35 PRMU2016-198
Manifold learning algorithms try to find the low dimensional representation of high dimensional data for the visualizati... [more] BioX2016-35 PRMU2016-198
IBISML 2016-11-17
Kyoto Kyoto Univ. [Poster Presentation] Analysis of Multimodal Deep Neural Networks -- Towards the elucidation of the modality integration mechanism --
Yoh-ichi Mototake, Takashi Ikegami (unit of Tokyo) IBISML2016-97
With the rapid development of information technology in recent years,
several machine learning algorithms that integra... [more]
PRMU, SP, WIT, ASJ-H 2016-06-13
Tokyo   Preliminary study on deep manifold embedding for 3D object pose estimation
Hiroshi Ninomiya, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase (Nagoya Univ.), Norimasa Kobori, Kunimatsu Hashimoto (Toyota) PRMU2016-39 SP2016-5 WIT2016-5
Recently, 3D object pose estimation is being focused. The parametric eigenspace method is known as one of the fundamenta... [more] PRMU2016-39 SP2016-5 WIT2016-5
IBISML 2016-03-18
Tokyo Institute of Statistical Mathematics Dimension Estimation of Topological Manifolds based on Measure of Simplexes and Application to Manifold Learning
Hajime Tasaki, Jinhui Chao (Chuo Univ.) IBISML2015-102
Dimension reduction is one of the most important issues in machine learning and computational intelligence for reduction... [more] IBISML2015-102
NLP 2015-11-01
Okinawa Nobumoto Ohama Memorial Hall The hierarchical visualizing and learning method in the generative topographic mapping
Takehito Oshita (BCI), Mikio Hasegawa (Tokyo Univ. of Science) NLP2015-124
Generative Topographic Mapping(GTM) is the latent variable model which is formalized Kohonen’s Self-Organization Map in ... [more] NLP2015-124
MI 2014-01-26
Okinawa Bunka Tenbusu Kan Newborn brain growth model using manifold learning
Ryosuke Nakano (Univ. of Hyogo), Syoji Kobashi, Kei Kuramoto (Univ. of Hyogo/WPI-IFReC), Yuki Wakata, Kumiko Ando, Reiichi Ishikura (Hyogo College of Medicine), Tomomoto Ishikawa (Ishikawa Hospital), Shozo Hirota (Hyogo College of Medicine), Yutaka Hata (Univ. of Hyogo/Osaka Univ.) MI2013-64
To develop a computer-aided diagnosis system for neonatal cerebral disorders, some literatures have shown atlas-based me... [more] MI2013-64
NC 2012-10-04
Fukuoka Kyushu Institute of Technology (Wakamatsu Campus) Improvement of Gesture Recognition based on Higher-rank Self-Organizing Map with Graph-based Distance
Norihiro Fujita, Keiichi Horio (Kyushu Inst. of Tech.) NC2012-47
The authors proposed a gesture recognition method which is based on higher-rank self-organizing map. In the method, a ge... [more] NC2012-47
NC 2012-07-30
Shiga Ritsumeikan Univ. College of Information Science and Engineering Neuroevolution with Manifold Learning for Mario AI
Hisashi Handa (Kinki Univ.) NC2012-13
This talk presents a Neuroevolution with Manifold Learning for Mario AI championship. The Manifold Learning method provi... [more] NC2012-13
PRMU, MI, IE 2012-05-17
Aichi   Image Super-Resolution using Manifold Learning with Vector Quantization
Kazuki Taniguchi, Xian-Hua Han, Yutaro Iwamoto, So Sasatani, Yen-Wei Chen (Ritsumeikan Univ.) IE2012-19 PRMU2012-4 MI2012-4
Image Super-Resolution (SR) is to recover the lost high-frequency information from several or only one available image. ... [more] IE2012-19 PRMU2012-4 MI2012-4
NC 2012-01-26
Hokkaido Future University Hakodate The learning theory and algorithm of latent multi-dynamical systems -- Implementation by higher-order topographic mapping --
Tetsuo Furukawa, Takashi Ohkubo (Kyutech) NC2011-107
The purpose of this paper is to establish the learning theory of multiple dynamical systems, as well as to develop
the ... [more]
NLP 2011-03-10
Tokyo Tokyo University of Science A Manifold Learning Approach for Analyzing Chaos in A Dripping Faucet System
Hiromichi Suetani (Kagoshima Univ./JST/RIKEN), Hiroki Kuroiwa, Hiroki Hata (Kagoshima Univ.), Shotaro Akaho (AIST) NLP2010-173
Dripping water from a faucet is very familiar to us and it provides various nonlinear phenomena including chaos. When i... [more] NLP2010-173
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