This paper develops a novel online algorithm, namely moving average stochastic variational inference （MASVI）, which applies the results obtained by previous iterations to smooth out noisy natural gradients. We analy...
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This paper develops a novel online algorithm, namely moving average stochastic variational inference （MASVI）, which applies the results obtained by previous iterations to smooth out noisy natural gradients. We analyze the convergence property of the proposed algorithm and conduct a set of experiments on two large-scale collections that contain millions of documents. Experimental results indicate that in contrast to algorithms named ＇stochastic variational inference＇ and ＇SGRLD＇, our algorithm achieves a faster convergence rate and better performance.
In previous studies, non-distance-dependent surveillance strategies have improved the performance of contagious outbreaks detection. In this paper, we propose a new distance-dependent strategy that does not require as...
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ISBN:
(纸本)9781450335751
In previous studies, non-distance-dependent surveillance strategies have improved the performance of contagious outbreaks detection. In this paper, we propose a new distance-dependent strategy that does not require ascertainment of global or local network structure, namely, simply monitoring the relative significance difference of randomly selected individuals in school and workplace. To evaluate whether such two group could indeed provide early detection, we studied a flu outbreak in contact network simulation experiments. Our experimental results show that this method could provide significant additional time to react to epidemics, especially when the infection rate is not large.
As a new research direction in the field of database security, the technology of multilevel secure database is advancing by leaps and bounds. There are so many great multilevel secure relational models such as Bell-La...
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Point pattern matching is the basis of image recognition and computer vision. Point pattern matching in three dimensional space with the presence of noise and outlier is an important research focus. In this paper, we ...
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As a notoriously lethal human disease, cancer has obtained much concern for a long time. There have accumulated huge amounts of literature and experimental data on cancer-related research. It is impossible for people ...
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Real-time three-dimensional visualization for seismic data is difficult when seismic data are large-scale and usually exceed the limitation of host memories. This paper proposed a dynamic caching framework based on OC...
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We consider a modular method to reinforcement learning that represents uncertainty of model parameters by maintaining probability distributions over them. The algorithm we call MBDP (model-based Bayesian dynamic progr...
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ISBN:
(纸本)9781509001644
We consider a modular method to reinforcement learning that represents uncertainty of model parameters by maintaining probability distributions over them. The algorithm we call MBDP (model-based Bayesian dynamic programming) can be decomposed into two parallel types of inference: model learning and policy learning. During learning a model, we update posterior distributions of a model over observations after taking an action in each state. During learning a policy, we solve MDPs by dynamic programming with greedy approximation to make an agent choose behaviors which maximize return under the estimated model. Furthermore, we propose a principled method which utilizes the variance of Dirichlet distributions for determining when to learn and relearn the model. We demonstrate that MBDP can find near optimal policies with high probability by sufficient model learning and experimental results show that MBDP performs better compared with current state-of-the-art methods in reinforcement learning.
In this paper, a new image segmentation algorithm based on Otsu thresholding. One of attractive feature of this algorithm is its ability of processing noised images. The framework contains three steps: the image to be...
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In this paper, we investigate the problem of image de-noising. Here, the theory of morphological component analysis is employed to separate the image to be de-noised into some layers with different morphological compo...
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In this paper, a new image de-noising algorithm based on directional bi-dimensional empirical mode decomposition. Attractive features of this algorithm include its data driven mechanism and its ability of capturing di...
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