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Corrected Mean-Covariance RBMs and General High-Order Semi-RBMs for Large-Scale Collaborative Filtering and Prediction
Corrected Mean-Covariance RBMs and General High-Order Semi-RBMs for Large-Scale Collaborative Filtering and Prediction
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机译:大规模均值协方差RBM和一般高阶半RBM的协同过滤和预测
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摘要
Systems and methods are disclosed for operating a Restricted Boltzmann Machine (RBM) by determining a corrected energy function of high-order semi-RBMs (hs-RBMs) without self-interaction; performing distributed pre-training of the hs-RBM; adjusting weights of the hs-RBM using contrastive divergence; generating predictions by Gibbs Sampling or by determining conditional probabilities with hidden units integrated out; and generating predictions.
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