Zobrazeno 1 - 10
of 87
pro vyhledávání: '"Takeda, Koujin"'
Autor:
Endo, Yusuke, Takeda, Koujin
Publikováno v:
Neural Computation (2024) 36 (11) 2540-2570
We propose a new method of independent component analysis (ICA) in order to extract appropriate features from high-dimensional data. In general, matrix factorization methods including ICA have a problem regarding the interpretability of extracted fea
Externí odkaz:
http://arxiv.org/abs/2410.13171
Autor:
Endo, Yusuke, Takeda, Koujin
Publikováno v:
Neural Computation (2024) 36 (1) 128-150
In the study of the brain, there is a hypothesis that sparse coding is realized in information representation of external stimuli, which is experimentally confirmed for visual stimulus recently. However, unlike the specific functional region in the b
Externí odkaz:
http://arxiv.org/abs/2312.08809
Autor:
Kawasumi, Ryota, Takeda, Koujin
Publikováno v:
Neural Computation (2023) 35 (6) 1086-1099
We study the problem of hyperparameter tuning in sparse matrix factorization under Bayesian framework. In the prior work, an analytical solution of sparse matrix factorization with Laplace prior was obtained by variational Bayes method under several
Externí odkaz:
http://arxiv.org/abs/2305.10114
Autor:
Kimura, Shun, Takeda, Koujin
Publikováno v:
PLoS ONE 18(6): e0287708, 2023
Various brain functions that are necessary to maintain life activities materialize through the interaction of countless neurons. Therefore, it is important to analyze functional neuronal network. To elucidate the mechanism of brain function, many stu
Externí odkaz:
http://arxiv.org/abs/2211.05634
Publikováno v:
J. Stat. Mech. (2021) 063501
Neuronal ensemble inference is a significant problem in the study of biological neural networks. Various methods have been proposed for ensemble inference from experimental data of neuronal activity. Among them, Bayesian inference approach with gener
Externí odkaz:
http://arxiv.org/abs/2105.09679
Autor:
Kitano, Hiroki, Takeda, Koujin
Publikováno v:
J. Phys. Soc. Jpn. 89, 043801 (2020)
We study text summarization from the viewpoint of maximum coverage problem. In graph theory, the task of text summarization is regarded as maximum coverage problem on bipartite graph with weighted nodes. In recent study, belief-propagation based algo
Externí odkaz:
http://arxiv.org/abs/2004.08301
Autor:
Kimura, Shun, Takeda, Koujin
Publikováno v:
Proceedings of NetSci-X 2020, pp.77-90
Neuronal ensemble inference is one of the significant problems in the study of biological neural networks. Various methods have been proposed for ensemble inference from their activity data taken experimentally. Here we focus on Bayesian inference ap
Externí odkaz:
http://arxiv.org/abs/1911.06509
Autor:
Kawasumi, Ryota, Takeda, Koujin
Publikováno v:
J. Stat. Mech. (2018) 053404
We derive analytical expression of matrix factorization/completion solution by variational Bayes method, under the assumption that observed matrix is originally the product of low-rank dense and sparse matrices with additive noise. We assume the prio
Externí odkaz:
http://arxiv.org/abs/1803.06234
Autor:
Endo, Yusuke, Takeda, Koujin
Publikováno v:
Neural Computation; Nov2024, Vol. 36 Issue 11, p2540-2570, 31p
Autor:
Kimura, Shun1 (AUTHOR), Takeda, Koujin1 (AUTHOR) koujin.takeda.kt@vc.ibaraki.ac.jp
Publikováno v:
PLoS ONE. 6/27/2023, Vol. 17 Issue 6, p1-25. 25p.