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 Results 1 - 20 of 34  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
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
PRMU, IBISML, IPSJ-CVIM [detail] 2023-03-03
16:55
Hokkaido Future University Hakodate
(Primary: On-site, Secondary: Online)
Upper bound of real log canonical threshold based on linear programming problem for the multi-indexes of a polynomial
Joe Hirose (Tokyo Tech) PRMU2022-125 IBISML2022-132
A real log canonical threshold (RLCT) is an invariant which gives a Bayesian generalization error. While a strict value ... [more] PRMU2022-125 IBISML2022-132
pp.363-370
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
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
SIS 2021-03-04
15:50
Online Online [Invited Talk] A Proposal in Order to use AI Safely for Control
Nobuyasu Kanekawa (Hitachi) SIS2020-50
The research of the deep learning which developed the "neural Network" proposed by the second AI (artificial intelligenc... [more] SIS2020-50
pp.83-87
MBE, NC, NLP, CAS
(Joint) [detail]
2020-10-30
10:50
Online Online statistical mechanical analysis of catastrophic forgetting in continual learning with teacher and student networks
Haruka Asanuma, Shiro Takagi, Yoshihiro Nagano, Yuki Yoshida (Tokyo Univ.), Yasuhiko Igarashi (Tsukuba Univ.), Masato Okada (Tokyo Univ.) NC2020-18
When single neural networks sequentially learns more than one task, catastrophic forgetting occurs except for the last t... [more] NC2020-18
pp.50-55
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
IBISML 2016-11-16
15:00
Kyoto Kyoto Univ. [Poster Presentation] Optimization Method of Deep Ensemble Learning using Hierarchical Clustering
Natsuki Koda, Sumio Watanabe (Tokyo Tech) IBISML2016-70
The method which is used for prediction by combining many different learning machines generated by using same training d... [more] IBISML2016-70
pp.171-176
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
NLP 2015-07-22
11:00
Hokkaido Bibai Onsen Yu-rinkan Optimization of Generalization Based on Genetic Algorithm -- Application for Function Analysis and Pattern Recognition --
Hideki Satoh (Future Univ. Hakodate), Daisuke Kasai (Tokachi Foundation), Masako Satoh (Insent) NLP2015-78
A function approximation method was developed taking generalization into account, and it was applied to a pattern recogn... [more] NLP2015-78
pp.57-62
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
TL 2012-12-08
16:25
Tokyo WASEDA University The effect of preemption on the transitivity alternations in L2 English
Takehiko Yagi, Takaaki Suzuki (Kyoto Sangyo Univ.) TL2012-46
This study investigates the effect of preemption on the transitivity alternations in second language acquisition of Engl... [more] TL2012-46
pp.71-76
IBISML 2011-11-10
15:45
Nara Nara Womens Univ. Nonparametric Estimation of Mixture Model and Minimum Divergence Methods
Kazuho Watanabe (NAIST), Shiro Ikeda (ISM) IBISML2011-78
We discuss a nonparametric estimation method of the mixing distribution in mixture models.
We propose an objective fun... [more]
IBISML2011-78
pp.243-249
DC 2011-10-20
13:30
Tokyo   Neighborhood Level Error Control Codes for Multiple-level Systems
Shohei Kotaki, Masato Kitakami (Chiba Univ.) DC2011-23
Multiple-level concept, which deals with more than 2 states as data unit, is used in such systems as Flash Memory or mod... [more] DC2011-23
pp.19-24
NC 2011-10-20
13:10
Fukuoka Ohashi Campus, Kyushu Univ. Statistical Mechanics of Node-Perturbation Learning for Nonlinear Perceptron
Kazuyuki Hara (Nihon Univ.), Kentaro Katahira (JST), Kazuo Okanoya (RIKEN), Masato Okada (Tokyo Univ.) NC2011-63
Node-perturbation learning is a kind of statistical gradient descent algorithm that can be applied to problems where the... [more] NC2011-63
pp.107-112
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 2011-03-29
16:30
Osaka Nakanoshima Center, Osaka Univ. Fast Convergence Rate of Multiple Kernel Learning with Elastic-net Regularization
Taiji Suzuki, Ryota Tomioka (Univ. of Tokyo), Masashi Sugiyama (Tokyo Inst. of Tech.) IBISML2010-126
We investigate the learning rate of multiple kernel leaning (MKL)
with elastic-net regularization,
which consists of a... [more]
IBISML2010-126
pp.153-160
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