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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 65  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
NC, MBE
(Joint)
2020-03-05
13:00
Tokyo University of Electro Communications
(Cancelled but technical report was issued)
Bayesian learning curve for the case when the optimal distribution is not unique
Shuya Nagayasu, Sumio Watanabe (Tokyo Tech) NC2019-94
Bayesian inference is a widely used statistical method. Asymptotic behaviors of generalization loss and free energy in B... [more] NC2019-94
pp.107-112
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
MBE, NC
(Joint)
2018-03-14
10:25
Tokyo Kikai-Shinko-Kaikan Bldg. Experimental Analysis of Real Log Canonical Threshold in Stochastic Matrix Factorization using Hamiltonian Monte Carlo Method
Naoki Hayashi, Sumio Watanabe (Tokyo Tech) NC2017-89
For the real log canonical threshold (RLCT) that gives the Bayesian generalization error of stochastic matrix factorizat... [more] NC2017-89
pp.127-131
IBISML 2018-03-05
13:00
Fukuoka Nishijin Plaza, Kyushu University Real Log Canonical Threshold and Bayesian Generalization Error of Mixture of Poisson Distributions
Kenichiro Sato, Sumio Watanabe (Tokyo Inst. of Tech.) IBISML2017-90
 [more] IBISML2017-90
pp.1-6
IBISML 2017-11-09
13:00
Tokyo Univ. of Tokyo [Poster Presentation] Real Log Canonical Threshold of Stochastic Matrix Factorization and its Application to Bayesian Learning
Naoki Hayashi, Sumio Watanabe (TokyoTech) IBISML2017-38
In stochastic matrix factorization (SMF), we deal with problems that we predict an observed stochastic matrix as a produ... [more] IBISML2017-38
pp.23-30
MBE, NC
(Joint)
2017-03-13
10:00
Tokyo Kikai-Shinko-Kaikan Bldg. Experimental Analysis of Real Log Canonical Threshold in Non-negative Matrix Factorization
Naoki Hayashi, Sumio Watanabe (Tokyo Tech) NC2016-78
For the real log canonical threshold ( RLCT ) that gives the Bayesian generalization error of non-negative matrix factor... [more] NC2016-78
pp.85-90
IBISML 2016-11-16
15:00
Kyoto Kyoto Univ. Estimation of Vehicular Headway-Velocity Characteristics in Mixture of Piecewise Linear Model using Variational Bayes Method
Fumito Nakamura, Sumio Watanabe (Tokyo Tech) IBISML2016-65
 [more] IBISML2016-65
pp.137-142
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
IBISML 2016-11-17
14:00
Kyoto Kyoto Univ. [Poster Presentation] A real log canonical threshold of nonnegative matrix factorization and its application to Bayesian learning
Naoki Hayashi, Sumio Watanabe (Tokyo Tech) IBISML2016-76
In nonnegative matrix factorization(NMF),we deal with problems that we predict a data matrix as a product of two
nonne... [more]
IBISML2016-76
pp.215-220
MBE, NC
(Joint)
2016-03-23
13:10
Tokyo Tamagawa University Evaluation Method of Free Energy Calculation by Replica Monte Carlo Method using Nongaussian and Solvable Models
Shoji Sugai, Sumio Watanabe (Tokyo Tech) NC2015-83
 [more] NC2015-83
pp.77-82
IBISML 2016-03-17
15:45
Tokyo Institute of Statistical Mathematics Learning and Generalization in Neural Networks using Hamiltonian Monte Carlo Method
Fumito Nakamura, Sumio Watanabe (Tokyo Tech) IBISML2015-97
 [more] IBISML2015-97
pp.25-29
IBISML 2015-11-26
15:00
Ibaraki Epochal Tsukuba [Poster Presentation] Classification of Training Results in Nonlinear Multi-Layer Principal Component Analysis using Sparse Representation
Natsuki Koda, Sumio Watanabe (Tokyo Tech) IBISML2015-55
The bottleneck neural network or Nonlinear Multi-Layer Principal Component Analysis(NMPCA) is used to extract the low di... [more] IBISML2015-55
pp.19-24
NC, MBE 2015-03-17
13:50
Tokyo Tamagawa University Optimization of LASSO Learning using WAIC and Its Application to City Data Analysis
Dai Miyazaki, Sumio Watanabe (Tokyo Tech) MBE2014-175 NC2014-126
LASSO(Least Absolute Shrinkage and Selection Operator) is a method adding a penalty term consisting of absolute values o... [more] MBE2014-175 NC2014-126
pp.331-336
IBISML 2014-11-18
15:00
Aichi Nagoya Univ. [Poster Presentation] Optimization Method of LASSO Hyperparameter using WAIC
Dai Miyazaki, Sumio Watanabe (Tokyo Tech) IBISML2014-63
LASSO (Least Absolute Shrinkage and Selection Operator) was proposed as a regression method using a penalty term made of... [more] IBISML2014-63
pp.213-218
SP, IPSJ-SLP
(Joint)
2014-07-25
13:20
Iwate Hotel Hanamaki [Invited Talk] Evaluation Criteria of Statistical Learning when Gaussian Approximation can not be Applied to Likelihood Function
Sumio Watanabe (Tokyo Inst. of Tech.) SP2014-68
Conventional statistical asymptotic theory was established based on the assumption that the likelihood function can be a... [more] SP2014-68
pp.31-36
NC, MBE
(Joint)
2014-03-18
13:40
Tokyo Tamagawa University Computational validation of the information criterion WBIC by the exchange Monte Carlo method
Satoru Tokuda, Kenji Nagata (Univ. of Tokyo), Sumio Watanabe (Tokyo Inst. of Tech.), Masato Okada (Univ. of Tokyo/RIKEN) NC2013-109
In the models with hierarchy like artificial neural networks and mixture models, asymptotic normality, which AIC and BIC... [more] NC2013-109
pp.121-126
NC, MBE
(Joint)
2013-12-21
13:30
Gifu Gifu University Difference of Enough Nmbers for General and Regular Asymptotic Theories in Statistical Learning
Sumio Watanabe (Tokyo Tech) NC2013-61
There are two asymptotic theories in statistical learning. One is the regular theory which assumes that the likelihood f... [more] NC2013-61
pp.47-52
IBISML 2013-11-12
15:45
Tokyo Tokyo Institute of Technology, Kuramae-Kaikan [Poster Presentation] Model Selection of Layered Neural Networks using WBIC based on Steepest Descent and MCMC Method
Yusuke Tamai, Sumio Watanabe (Tokyo Inst. of Tech.) IBISML2013-36
Many learning machines such as neural networks, normal mixtures, and hidden Markov Models contain hierarchical layers, h... [more] IBISML2013-36
pp.1-6
MBE, NC
(Joint)
2013-03-13
10:15
Tokyo Tamagawa University Evaluation Method of Singular Cases Using Posterior Log Likelihood Ratio
Takashi Kato, Sumio Watanabe (Tokyo Inst. of Tech.) NC2012-134
 [more] NC2012-134
pp.1-6
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