Sumio Watanabe

According to our database, Sumio Watanabe authored at least 98 papers between 1992 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Free energy of Bayesian Convolutional Neural Network with Skip Connection.

Shuya Nagayasu, Sumio Watanabe

CoRR, 2023

Bayesian Free Energy of Deep ReLU Neural Network in Overparametrized Cases.

Shuya Nagayasu, Sumio Watanabe

CoRR, 2023

Upper Bound of Real Log Canonical Threshold of Tensor Decomposition and its Application to Bayesian Inference.

Naoki Yoshida, Sumio Watanabe

CoRR, 2023

2022

Asymptotic behavior of free energy when optimal probability distribution is not unique.

Shuya Nagayasu, Sumio Watanabe

Neurocomputing, 2022

Recent Advances in Algebraic Geometry and Bayesian Statistics.

Sumio Watanabe

CoRR, 2022

Mathematical Theory of Bayesian Statistics for Unknown Information Source.

Sumio Watanabe

CoRR, 2022

Asymptotic Behavior of Bayesian Generalization Error in Multinomial Mixtures.

Takumi Watanabe, Sumio Watanabe

CoRR, 2022

2020

Asymptotic Bayesian Generalization Error in Latent Dirichlet Allocation and Stochastic Matrix Factorization.

Naoki Hayashi, Sumio Watanabe

SN Comput. Sci., 2020

Testing Homogeneity for Normal Mixture Models: Variational Bayes Approach.

Natsuki Kariya, Sumio Watanabe

IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2020

2017

Upper bound of Bayesian generalization error in non-negative matrix factorization.

Naoki Hayashi, Sumio Watanabe

Neurocomputing, 2017

Upper Bound of Bayesian Generalization Error in Stochastic Matrix Factorization.

Naoki Hayashi, Sumio Watanabe

CoRR, 2017

Tighter upper bound of real log canonical threshold of non-negative matrix factorization and its application to Bayesian inference.

Naoki Hayashi, Sumio Watanabe

Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

Phase Transition Structure of Variational Bayesian Nonnegative Matrix Factorization.

Masahiro Kohjima, Sumio Watanabe

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2017, 2017

2016

Interpretation Method of Nonlinear Multilayer Principal Component Analysis by Using Sparsity and Hierarchical Clustering.

Natsuki Koda, Sumio Watanabe

Proceedings of the 15th IEEE International Conference on Machine Learning and Applications, 2016

2015

Bayesian Cross Validation and WAIC for Predictive Prior Design in Regular Asymptotic Theory.

Sumio Watanabe

CoRR, 2015

2013

A widely applicable Bayesian information criterion.

Sumio Watanabe

J. Mach. Learn. Res., 2013

A Geometric Evaluation of Self-Organizing Map and Application to City Data Analysis.

Shigehiro Ohara, Keisuke Yamazaki, Sumio Watanabe

Proceedings of the Multi-disciplinary Trends in Artificial Intelligence, 2013

2012

Statistical Learning Theory of Quasi-Regular Cases.

Koshi Yamada, Sumio Watanabe

IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2012

Information criterion for variational Bayes learning in regular and singular cases.

Koshi Yamada, Sumio Watanabe

Proceedings of the 6th International Conference on Soft Computing and Intelligent Systems (SCIS), 2012

2011

Two design methods of hyperparameters in variational Bayes learning for Bernoulli mixtures.

Daisuke Kaji, Sumio Watanabe

Neurocomputing, 2011

2010

Asymptotic analysis of Bayesian generalization error with Newton diagram.

Keisuke Yamazaki, Miki Aoyagi, Sumio Watanabe

Neural Networks, 2010

Equations of states in singular statistical estimation.

Sumio Watanabe

Neural Networks, 2010

Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory.

Sumio Watanabe

J. Mach. Learn. Res., 2010

Equations of States in Statistical Learning for an Unrealizable and Regular Case.

Sumio Watanabe

IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2010

Asymptotic Learning Curve and Renormalizable Condition in Statistical Learning Theory

Sumio Watanabe

CoRR, 2010

Phase Transition of Variational Bayes Learning in Bernoulli Mixture.

Daisuke Kaji, Kazuho Watanabe, Sumio Watanabe

Aust. J. Intell. Inf. Process. Syst., 2010

On Generalization Error of Self-Organizing Map.

Fumiaki Saitoh, Sumio Watanabe

Proceedings of the Neural Information Processing. Models and Applications, 2010

2009

Accuracy of Loopy belief propagation in Gaussian models.

Yu Nishiyama, Sumio Watanabe

Neural Networks, 2009

Upper bound for variational free energy of Bayesian networks.

Kazuho Watanabe, Motoki Shiga, Sumio Watanabe

Mach. Learn., 2009

Equations of States in Statistical Learning for a Nonparametrizable and Regular Case

Sumio Watanabe

CoRR, 2009

A Limit Theorem in Singular Regression Problem

Sumio Watanabe

CoRR, 2009

Optimal Hyperparameters for Generalized Learning and Knowledge Discovery in Variational Bayes.

Daisuke Kaji, Sumio Watanabe

Proceedings of the Neural Information Processing, 16th International Conference, 2009

2008

Exchange Monte Carlo Sampling From Bayesian Posterior for Singular Learning Machines.

Kenji Nagata, Sumio Watanabe

IEEE Trans. Neural Networks, 2008

Asymptotic behavior of exchange ratio in exchange Monte Carlo method.

Kenji Nagata, Sumio Watanabe

Neural Networks, 2008

A formula of equations of states in singular learning machines.

Sumio Watanabe

Proceedings of the International Joint Conference on Neural Networks, 2008

Design of Exchange Monte Carlo Method for Bayesian Learning in Normal Mixture Models.

Kenji Nagata, Sumio Watanabe

Proceedings of the Advances in Neuro-Information Processing, 15th International Conference, 2008

Experimental Study of Ergodic Learning Curve in Hidden Markov Models.

Masashi Matsumoto, Sumio Watanabe

Proceedings of the Advances in Neuro-Information Processing, 15th International Conference, 2008

Model Selection Method for AdaBoost Using Formal Information Criteria.

Daisuke Kaji, Sumio Watanabe

Proceedings of the Advances in Neuro-Information Processing, 15th International Conference, 2008

Generalization of Concave and Convex Decomposition in Kikuchi Free Energy.

Yu Nishiyama, Sumio Watanabe

Proceedings of the Artificial Neural Networks, 2008

On the Minima of Bethe Free Energy in Gaussian Distributions.

Yu Nishiyama, Sumio Watanabe

Proceedings of the Artificial Intelligence and Soft Computing, 2008

2007

Stochastic complexity for mixture of exponential families in generalized variational Bayes.

Kazuho Watanabe, Sumio Watanabe

Theor. Comput. Sci., 2007

Stochastic complexities of general mixture models in variational Bayesian learning.

Kazuho Watanabe, Sumio Watanabe

Neural Networks, 2007

Variational Bayes Solution of Linear Neural Networks and Its Generalization Performance.

Shinichi Nakajima, Sumio Watanabe

Neural Comput., 2007

The Calculation Method of Learning Coefficients by Weighted Resolution of Singularities.

Takeshi Matsuda, Sumio Watanabe

Proceedings of the 2007 International Conference on Machine Learning; Models, 2007

Resolution of Singularities and Stochastic Complexity of Complete Bipartite Graph-Type Spin Model in Bayesian Estimation.

Miki Aoyagi, Sumio Watanabe

Proceedings of the Modeling Decisions for Artificial Intelligence, 2007

Experimental Bayesian Generalization Error of Non-regular Models under Covariate Shift.

Keisuke Yamazaki, Sumio Watanabe

Proceedings of the Neural Information Processing, 14th International Conference, 2007

Experimental Analysis of Exchange Ratio in Exchange Monte Carlo Method.

Kenji Nagata, Sumio Watanabe

Proceedings of the Neural Information Processing, 14th International Conference, 2007

Asymptotic Bayesian generalization error when training and test distributions are different.

Keisuke Yamazaki, Motoaki Kawanabe, Sumio Watanabe, Masashi Sugiyama, Klaus-Robert Müller

Proceedings of the Machine Learning, 2007

Theoretical Analysis of Accuracy of Gaussian Belief Propagation.

Yu Nishiyama, Sumio Watanabe

Proceedings of the Artificial Neural Networks, 2007

Generalization Error of Automatic Relevance Determination.

Shinichi Nakajima, Sumio Watanabe

Proceedings of the Artificial Neural Networks, 2007

Algebraic Geometric Study of Exchange Monte Carlo Method.

Kenji Nagata, Sumio Watanabe

Proceedings of the Artificial Neural Networks, 2007

On a Singular Point to Contribute to a Learning Coefficient and Weighted Resolution of Singularities.

Takeshi Matsuda, Sumio Watanabe

Proceedings of the Artificial Neural Networks, 2007

Estimation of Poles of Zeta Function in Learning Theory Using Padé Approximation.

Ryosuke Iriguchi, Sumio Watanabe

Proceedings of the Artificial Neural Networks, 2007

Almost All Learning Machines are Singular.

Sumio Watanabe

Proceedings of the IEEE Symposium on Foundations of Computational Intelligence, 2007

Analysis of Exchange Ratio for Exchange Monte Carlo Method.

Kenji Nagata, Sumio Watanabe

Proceedings of the IEEE Symposium on Foundations of Computational Intelligence, 2007

2006

Stochastic Complexities of Gaussian Mixtures in Variational Bayesian Approximation.

Kazuho Watanabe, Sumio Watanabe

J. Mach. Learn. Res., 2006

Generalization Performance of Subspace Bayes Approach in Linear Neural Networks.

Shinichi Nakajima, Sumio Watanabe

IEICE Trans. Inf. Syst., 2006

The Exchange Monte Carlo Method for Bayesian Learning in Singular Learning Machines.

Kenji Nagata, Sumio Watanabe

Proceedings of the International Joint Conference on Neural Networks, 2006

Upper Bounds for Variational Stochastic Complexities of Bayesian Networks.

Kazuho Watanabe, Motoki Shiga, Sumio Watanabe

Proceedings of the Intelligent Data Engineering and Automated Learning, 2006

Generalization Performance of Exchange Monte Carlo Method for Normal Mixture Models.

Kenji Nagata, Sumio Watanabe

Proceedings of the Intelligent Data Engineering and Automated Learning, 2006

Localized Bayes Estimation for Non-identifiable Models.

Shingo Takamatsu, Shinichi Nakajima, Sumio Watanabe

Proceedings of the Neural Information Processing, 13th International Conference, 2006

Asymptotic Behavior of Stochastic Complexity of Complete Bipartite Graph-Type Boltzmann Machines.

Yu Nishiyama, Sumio Watanabe

Proceedings of the Neural Information Processing, 13th International Conference, 2006

Free Energy of Stochastic Context Free Grammar on Variational Bayes.

Tikara Hosino, Kazuho Watanabe, Sumio Watanabe

Proceedings of the Neural Information Processing, 13th International Conference, 2006

A Model Selection Method Based on Bound of Learning Coefficient.

Keisuke Yamazaki, Kenji Nagata, Sumio Watanabe, Klaus-Robert Müller

Proceedings of the Artificial Neural Networks, 2006

Analytic Solution of Hierarchical Variational Bayes in Linear Inverse Problem.

Shinichi Nakajima, Sumio Watanabe

Proceedings of the Artificial Neural Networks, 2006

Analytic Equivalence of Bayes a Posteriori Distributions.

Takeshi Matsuda, Sumio Watanabe

Proceedings of the Artificial Neural Networks, 2006

Generalization error of three layered learning model in bayesian estimation.

Miki Aoyagi, Sumio Watanabe

Proceedings of the Second IASTED International Conference on Computational Intelligence, 2006

2005

Singularities in complete bipartite graph-type Boltzmann machines and upper bounds of stochastic complexities.

Keisuke Yamazaki, Sumio Watanabe

IEEE Trans. Neural Networks, 2005

Stochastic complexities of reduced rank regression in Bayesian estimation.

Miki Aoyagi, Sumio Watanabe

Neural Networks, 2005

Algebraic geometry and stochastic complexity of hidden Markov models.

Keisuke Yamazaki, Sumio Watanabe

Neurocomputing, 2005

Algebraic geometry of singular learning machines and symmetry of generalization and training errors.

Sumio Watanabe

Neurocomputing, 2005

Variational Bayesian Stochastic Complexity of Mixture Models.

Kazuho Watanabe, Sumio Watanabe

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Generalization Error of Linear Neural Networks in an Empirical Bayes Approach.

Shinichi Nakajima, Sumio Watanabe

Proceedings of the IJCAI-05, Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, Edinburgh, Scotland, UK, July 30, 2005

Stochastic Complexity for Mixture of Exponential Families in Variational Bayes.

Kazuho Watanabe, Sumio Watanabe

Proceedings of the Algorithmic Learning Theory, 16th International Conference, 2005

2004

Newton Diagram and Stochastic Complexity in Mixture of Binomial Distributions.

Keisuke Yamazaki, Sumio Watanabe

Proceedings of the Algorithmic Learning Theory, 15th International Conference, 2004

Estimation of the Data Region Using Extreme-Value Distributions.

Kazuho Watanabe, Sumio Watanabe

Proceedings of the Algorithmic Learning Theory, 15th International Conference, 2004

2003

Singularities in mixture models and upper bounds of stochastic complexity.

Keisuke Yamazaki, Sumio Watanabe

Neural Networks, 2003

Learning Coefficients of Layered Models When the True Distribution Mismatches the Singularities.

Sumio Watanabe, Shun-ichi Amari

Neural Comput., 2003

Stochastic Complexity of Bayesian Networks.

Keisuke Yamazaki, Sumio Watanabe

Proceedings of the UAI '03, 2003

Stochastic complexities of hidden Markov models.

Keisuke Yamazaki, Sumio Watanabe

Proceedings of the NNSP 2003, 2003

2002

Errata to "learning efficiency of redundant neural networks in bayesian estimation".

Sumio Watanabe

IEEE Trans. Neural Networks, 2002

The Effect of Singularities in a Learning Machine when the True Parameters Do Not Lie on such Singularities.

Sumio Watanabe, Shun-ichi Amari

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

2001

Learning efficiency of redundant neural networks in Bayesian estimation.

Sumio Watanabe

IEEE Trans. Neural Networks, 2001

Algebraic geometrical methods for hierarchical learning machines.

Sumio Watanabe

Neural Networks, 2001

Algebraic Analysis for Nonidentifiable Learning Machines.

Sumio Watanabe

Neural Comput., 2001

2000

Algebraic Information Geometry for Learning Machines with Singularities.

Sumio Watanabe

Proceedings of the Advances in Neural Information Processing Systems 13, 2000

1999

Algebraic Analysis for Non-regular Learning Machines.

Sumio Watanabe

Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

Algebraic Analysis for Singular Statistical Estimation.

Sumio Watanabe

Proceedings of the Algorithmic Learning Theory, 10th International Conference, 1999

1998

Inequalities of Generalization Errors for Layered Neural Networks in Bayesian Learning.

Sumio Watanabe

Proceedings of the Fifth International Conference on Neural Information Processing, 1998

Probabilistic design.

Sumio Watanabe, Kenji Fukumizu

Proceedings of the Algorithms and Architectures., 1998

1996

A network of chaotic elements for information processing.

Shin Ishii, Kenji Fukumizu, Sumio Watanabe

Neural Networks, 1996

Solvable models of layered neural networks based on their differential structure.

Sumio Watanabe

Adv. Comput. Math., 1996

1995

Probabilistic design of layered neural networks based on their unified framework.

Sumio Watanabe, Kenji Fukumizu

IEEE Trans. Neural Networks, 1995

Genetic algorithms applied to bayesian image restoration.

Kazunori Takatsu, Hidefumi Sawai, Sumio Watanabe, Masahide Yoneyama

Syst. Comput. Jpn., 1995

A Modified Information Criterion for Automatic Model and Parameter Selection in Neural Network Learning.

Sumio Watanabe

IEICE Trans. Inf. Syst., 1995

1993

Solvable Models of Artificial Neural Networks.

Sumio Watanabe

Proceedings of the Advances in Neural Information Processing Systems 6, 1993

An Optimization Method of Layered Neural Networks based on the Modified Information Criterion.

Sumio Watanabe

Proceedings of the Advances in Neural Information Processing Systems 6, 1993

1992

An ultrasonic visual sensor for three-dimensional object recognition using neural networks.

Sumio Watanabe, Masahide Yoneyama

IEEE Trans. Robotics Autom., 1992