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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MI | 2024-03-03 16:54 |
Okinawa | OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
Domain generalization with WSI feature Yuki Shigeyasu (Kyushu Univ.), Shota Harada (Hiroshima City Univ.), Mariyo Kurata, Kazuhiro Terada, Naoki Nakazima (Kyoto Univ.), Akihiko Yoshizawa (Nara Medical Univ.), Hiroyuki Abe, Tetsuo Ushiku (Tokyo Univ.), Ryoma Bise (Kyushu Univ.)  MI2023-58 |
In this study, we propose a domain generalization method for pathological images (WSI). Domain shifts in pathological im... [more] | MI2023-58 pp.81-84 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2023-06-30 11:10 |
Okinawa | OIST Conference Center (Primary: On-site, Secondary: Online) |
On performance degradation of a method by minimizing the conditional mutual information for the out-of-distribution generalization Genki Takahashi, Toshiyuki Tanaka (Kyoto University) NC2023-15 IBISML2023-15 |
In the out-of-distribution generalization problem, the smaller the degree of change in the data generating distribution ... [more] | NC2023-15 IBISML2023-15 pp.91-97 |
| IBISML | 2022-12-22 15:10 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
Effect of Prior Distribution when Data-Generating Process is in a Neighborhood of Singularities of Learning Machines Nozomi Maki, Sumio Watanabe (TokyoTech) IBISML2022-46 |
Learning machines which have hierarchical structure or latent variables such as deep learning or normal mixtures contain... [more] | IBISML2022-46 pp.18-23 |
| IBISML | 2022-03-08 10:55 |
Online | Online | Real log canonical threshold of reduced rank regression when inputs are on a low dimensional hyperplane Joe Hirose, Sumio Watanabe (Tokyo Tech) IBISML2021-32 |
A reduced rank regression is a statistical model which estimates a linear regression function from in- puts to outputs w... [more] | IBISML2021-32 pp.15-18 |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-21 13:15 |
Online | Online | Towards Universal Deep Image Compression Koki Tsubota (UTokyo), Hiroaki Akutsu (Hitachi), Kiyoharu Aizawa (UTokyo) ITS2021-31 IE2021-40 |
In this paper, we investigate deep image compression towards universal usage. In image compression, it is desirable to b... [more] | ITS2021-31 IE2021-40 pp.37-42 |
| RCS | 2021-06-24 13:00 |
Online | Online | A Study on Recurrent Neural Network Aided GNSS Positioning Kohei Nishioka, Shinsuke Ibi (Doshisha Univ.), Takumi Takahashi (Osaka Univ.), Hisato Iwai (Doshisha Univ.) RCS2021-53 |
One method of positioning schemes with the aid of the global navigation satellite system (GNSS) is to approximately solv... [more] | RCS2021-53 pp.145-150 |
| IBISML | 2020-01-09 13:00 |
Tokyo | ISM | Asymptotic Behavior of Bayesian Generalization Error in Multinomial Mixtures Takumi Watanabe, Sumio Watanabe (Tokyo Tech) IBISML2019-18 |
Multinomial mixtures are widely used in the information engineering field. However, it is not subject to the conventiona... [more] | IBISML2019-18 pp.1-8 |
| IBISML | 2020-01-09 13:25 |
Tokyo | ISM | Real Log Canonical Threshold of Three Layered Neural Network with Swish Activation Function Raiki Tanaka, Sumio Watanabe (Tokyo Tech) IBISML2019-19 |
In neural network learning, it is known that selection of activation function effects generalization performance. Althou... [more] | IBISML2019-19 pp.9-15 |
| NC, NLP (Joint) |
2016-01-29 15:50 |
Fukuoka | Kyushu Institute of Technology | Node-perturbation Learning for Soft-committee machine Kazuyuki Hara (Nihon Univ.), Kentaro Katahira (Nagoya Univ.) NC2015-66 |
Node perturbation learning is a stochastic gradient descent method for neural networks. It estimates the gradient of the... [more] | NC2015-66 pp.49-54 |
| MICT, WBS | 2014-07-29 12:30 |
Osaka | Osaka City Univ. (Umeda Satellite) | [Poster Presentation]
A Study of LED-to-LED Visible Light Communication with Generalization Modified Prime Sequence Code Naoya Murata, Yusuke Kozawa, Yohtaro Umeda (Tokyo Univ. of Science), Hiromasa Habuchi (Ibaraki Univ.) WBS2014-21 MICT2014-35 |
In this paper, an LED-to-LED VLCs (Visible Light Communication) system with generalized modified prime sequence codes (G... [more] | WBS2014-21 MICT2014-35 pp.67-72 |
| NC, MBE (Joint) |
2013-07-19 14:30 |
Tokushima | The University of Tokushima | Statistical Mechanics of node-perturbation Learning using two independent noises Kazuyuki Hara (Nihon Univ.), Kentaro Katahira, Masato Okada (Univ. of Tokyo) NC2013-17 |
Node perturbation learning is a stochastic gradient descent method for neural networks. It estimates the gradient by com... [more] | NC2013-17 pp.13-18 |
| NC, IPSJ-BIO [detail] | 2011-06-24 14:15 |
Okinawa | 50th Anniversary Memorial Hall, University of the Ryukyus | The Capability of Selective Desensitization Neural Networks at Two-Variable Function Approximation Kazuaki Nonaka, Fumihide Tanaka, Masahiko Morita (Tsukuba Univ) NC2011-14 |
Selective Desensitization Neural Network (SDNN) is known to be able to approximate some functions well with high general... [more] | NC2011-14 pp.113-118 |
| IBISML | 2010-11-05 15:30 |
Tokyo | IIS, Univ. of Tokyo | [Poster Presentation]
Statistical mechanics of on-line learning using correlated examples Kento Nakao (Kansai Univ.), Yuuta Narukawa (Daihen), Seiji Miyoshi (Kansai Univ.) IBISML2010-91 |
We consider a model composed of nonlinear perceptrons and analytically investigate the generalization performance of lea... [more] | IBISML2010-91 pp.239-244 |
| IBISML | 2010-11-05 15:30 |
Tokyo | IIS, Univ. of Tokyo | [Poster Presentation]
Statistical Mechanics of Adaptive Weight Perturbation Learning Ryousuke Miyoshi, Yutaka Maeda, Seiji Miyoshi (Kansai Univ.) IBISML2010-92 |
The weight perturbation learning was proposed as a learning rule which adds perturbation to the variable parameters of ... [more] | IBISML2010-92 pp.245-250 |
| NC, NLP | 2008-06-27 15:35 |
Okinawa | University of the Ryukyus | GA-based geometrical learning of binary neural networks and its generalization capability Syuhei Shimada, Hidehiro Nakano, Arata Miyauchi (Musashi Inst. Tech.) NC2008-27 |
GA-based geometrical learning of Binary Neural Network (BNN) is discussed in this paper. To apply BNN to the multi cla... [more] |
NC2008-27 pp.79-84 |
| NC, MBE (Joint) |
2008-03-13 13:30 |
Tokyo | Tamagawa Univ | A Statistical Analysis of Support Vector Machines of Forgetting Factor Yoshihiko Nomura, Hiroyuki Funaya, Kazushi Ikeda (Kyoto Univ.) NC2007-168 |
Support Vector Machines (SVMs) are, in general, trained in batch but any trick is necessary when the target to be traine... [more] | NC2007-168 pp.331-336 |
| NC | 2006-06-16 10:10 |
Okinawa | OIST | A Method of Data Classification of Bagging Using HRGA/P and Its Applications Hong Zhang, Masumi Ishikawa (K.I.T.) |
To obtain a classification model with high generalization ability, we propose to use a hybrid real-coded genetic algorit... [more] | NC2006-25 pp.19-24 |
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