26 citations · 48 across the 5 of their papers we have counts for
5 papers
Structure Learning of Markov Random Fields through Grow-Shrink Maximum Pseudolikelihood Estimation
Yuya Takashina, Shuyo Nakatani, Masato Inoue
Learning the structure of Markov random fields (MRFs) plays an important role in multivariate analysis. The importance has been increasing with the recent rise of statistical relat…
Posterior Mean Super-Resolution with a Compound Gaussian Markov Random Field Prior
Takayuki Katsuki, Masato Inoue
This manuscript proposes a posterior mean (PM) super-resolution (SR) method with a compound Gaussian Markov random field (MRF) prior. SR is a technique to estimate a spatially high…
Posterior Mean Super-resolution with a Causal Gaussian Markov Random Field Prior
Takayuki Katsuki, Akira Torii, Masato Inoue
We propose a Bayesian image super-resolution (SR) method with a causal Gaussian Markov random field (MRF) prior. SR is a technique to estimate a spatially high-resolution image fro…
An LDPCC decoding algorithm based on Bowman-Levin approximation --Comparison with BP and CCCP--
Masato Inoue, Miho Komiya, Yoshiyuki Kabashima
Belief propagation (BP) and the concave convex procedure (CCCP) are both methods that utilize the Bethe free energy as a cost function and solve information processing tasks. We ha…
On-Line Learning Theory of Soft Committee Machines with Correlated Hidden Units - Steepest Gradient Descent and Natural Gradient Descent -
Masato Inoue, Hyeyoung Park, Masato Okada
The permutation symmetry of the hidden units in multilayer perceptrons causes the saddle structure and plateaus of the learning dynamics in gradient learning methods. The correlati…