Garen Arevian

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This paper describes a learning news agent HyNeT which uses hybrid neural network techniques for classifying news titles as they appear on an internet newswire. Recurrent plausibility networks with local memory are developed and examined for learning robust text routing. HyNeT is described for the first time in this paper. We show that a careful hybrid(More)
The World Wide Web has been growing rapidly in recent years, along with increasing needs for contentbased webpage filtering. But most existing filtering systems cannot easily satisfy the personalized filtering demands from different users at the same time. In this paper, a customizable instance-driven webpage filtering strategy is proposed. For different(More)
This paper focuses on symbolic transducers and recurrent neural preference machines to support the task of mining and classifying textual information. These encoding symbolic transducers and learning neural preference machines can be seen as independent agents, each one tackling the same task in a different manner. Systems combining such machines can(More)
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