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Items: 5

1.

Variational inference with ARD prior for NIRS diffuse optical tomography.

Miyamoto A, Watanabe K, Ikeda K, Sato MA.

IEEE Trans Neural Netw Learn Syst. 2015 May;26(5):1109-14. doi: 10.1109/TNNLS.2014.2328576.

PMID:
25881370
2.

Variational Bayesian Inference Algorithms for Infinite Relational Model of Network Data.

Konishi T, Kubo T, Watanabe K, Ikeda K.

IEEE Trans Neural Netw Learn Syst. 2015 Sep;26(9):2176-81. doi: 10.1109/TNNLS.2014.2362012. Epub 2014 Oct 28.

PMID:
25361514
3.

Divergence measures and a general framework for local variational approximation.

Watanabe K, Okada M, Ikeda K.

Neural Netw. 2011 Dec;24(10):1102-9. doi: 10.1016/j.neunet.2011.06.004. Epub 2011 Jun 15.

PMID:
21719252
4.

Variational Bayesian mixture model on a subspace of exponential family distributions.

Watanabe K, Akaho S, Omachi S, Okada M.

IEEE Trans Neural Netw. 2009 Nov;20(11):1783-96. doi: 10.1109/TNN.2009.2029694. Epub 2009 Sep 18.

PMID:
19770092
5.

Stochastic complexities of general mixture models in variational Bayesian learning.

Watanabe K, Watanabe S.

Neural Netw. 2007 Mar;20(2):210-9. Epub 2006 Aug 10.

PMID:
16904288

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