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LEARNING AND ANALYZING VECTOR ENCODING OF SYMBOLIC REPRESENTATION
Roland Fernandez, Aslı C¸elikyılmaz, Paul Smolensky, Rishabh Singh
Feb 12, 2018 (modified: Jun 04, 2018)ICLR 2018 Workshop Submissionreaders: everyoneShow Bibtex
Abstract:We present a formal language with expressions denoting general symbol structures and queries which access information in those structures. A sequence-to-sequence network processing this language learns to encode symbol structures and query them. The learned representation (approximately) shares a simple linearity property with theoretical techniques for performing this task.
Keywords:Knowledge, Representation, Embedding, Symbolic Structure, Encoder Decoder, Deep Learning, Vector Space
TL;DR:We present an expression language for representing and querying structured symbolic information and use it to train an encoder decoder network to represent the expressions as fixed size decodable embedding vectors.
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