Qinliang Su

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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Abstract It is known that Gaussian belief propagation (BP) is a low-complexity algorithm for (approximately) computing the marginal distribution of a high dimensional Gaussian distribution. However,(More)
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Abstract In order to compute the marginal probability density function (PDF) with Gaussian belief propagation (BP), it is important to know whether it will converge in advance. By describing the(More)
We introduce the truncated Gaussian graphical model (TGGM) as a novel framework for designing statistical models for nonlinear learning. A TGGM is a Gaussian graphical model (GGM) with a subset of variables truncated to be nonneg-ative. The truncated variables are assumed latent and integrated out to induce a marginal model. We show that the variables in(More)
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Abstract Belief propagation (BP) is an efficient algorithm for calculating approximate marginal probability density function (PDF) in large-scale Gaussian graphical models. It is known that when BP(More)
Recurrent neural networks (RNNs) have shown promising performance for language modeling. However, traditional training of RNNs using back-propagation through time often suffers from overfit-ting. One reason for this is that stochastic optimization (used for large training sets) does not provide good estimates of model uncertainty. This paper leverages(More)
Gaussian graphical models (GGMs) are widely used for statistical modeling, because of ease of inference and the ubiquitous use of the normal distribution in practical approximations. However, they are also known for their limited modeling abilities , due to the Gaussian assumption. In this paper, we introduce a novel variant of GGMs, which relaxes the(More)
—A macrocell superposed by indoor deployed femto-cells forms a geography-overlapped and spectrum-shared two-tier network, which can efficiently improve coverage and enhance system capacity. To reduce inter-tier co-channel interference, femtocell user should choose suitable access channel according to the path losses between itself and the macrocell users.(More)
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