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)
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)
This paper explores the application of recurrent neural networks for the task of robust text classification of a real-world benchmarking corpus. There are many well-established approaches which are used for text classification, but they fail to address the challenge from a more multi-disciplinary viewpoint such as natural language processing and artificial(More)
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