Hasari Tosun

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Restricted Boltzmann Machines (RBM) are energy-based models that are successfully used as generative learning models as well as crucial components of Deep Belief Networks (DBN). The most successful training method to date for RBMs is the Contrastive Divergence method. However, Contrastive Divergence is inefficient when the number of features is very high(More)
—Deep learning is a popular field that encompasses a range of multi-layer connectionist techniques. While these techniques have achieved great success on a number of difficult computer vision problems, the representation biases that allow this success have not been thoroughly explored. In this paper, we examine the hypothesis that one strength of many deep(More)
—Current trust models for social networks commonly rely on explicit voting mechanisms where individuals vote for each other as a form of trust statement. However, there is a wealth of information about individuals beyond trust voting in emerging web based social networks. Incorporating sources of evidence into trust models for social networks has not been(More)
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