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Representation learning has emerged as a newly active research subject in many machine learning applications because of its excellent performance. In the context of natural language processing, paragraph (or sentence and document) embedding learning is more suitable/reasonable for some tasks, such as information retrieval and document summarization. However, as far as we are aware, there is only a...
A new method for solving adaptive Boolean compressive sensing is proposed. A conventional method determining a pool for each test by a simple Bernoulli random design has been proposed. However, the conventional method has the drawback that the probability of the exact recovery is smaller than that of the optimal case because the redundancy of the mixing matrix tends to be high. To avoid the high redundancy,...
3D image/video applications attract lots of attention these days. Due to increased data amount compared to 2D image/video, many researches try to well compress 3D data by removing the inter-view redundancy between different views. In general, only interpixel redundancy is exploited, not psychovisual redundancy. Currently, binocular just noticeable difference (BJND) model is proposed to measure the...
To comprise the advantages of both the spatial domain and transform domain methods, this paper presents a novel hybrid algorithm for multi-focus images fusion, which reduces the error rate of sub-band coefficients selection in the transform domain and reduce the artificial discontinuities created in the spatial domain algorithms. In this method, wavelet transforms are firstly performed on each input...
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