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We propose to use acoustic models with sparse inverse covariance matrices to deal with the well-known over-fitting problem of discriminative training, especially when training data are limited. Compared with traditional diagonal or full covariance models, significant improvement by using sparse inverse covariance matrices has been achieved with maximum likelihood training. In state-of-the-art large...
In this paper, we propose a new variational model for image denoising, which contains two regularizing term. One regularizing term is BV norm, another is formulated in terms of summation in curvelet domain. Solving this new variational model results in an iterative algorithm, which combines naturally curvelet shrinkage with nonlinear anisotropic diffusion equations. Numerical examples show that the...
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