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Pankajan Chanthirasegaran

2 accepted papers

2017

Learning Continuous Semantic Representations of Symbolic Expressions

ICLR 2017workshop

The question of how procedural knowledge is represented and inferred is a fundamental problem in machine learning and artificial intelligence. Recent work on program induction has proposed neural architectures, based on abstractions like stacks, Turing machines, and interpreters, that operate on ab…

Cited by 128SourceScholar
2017

Learning Continuous Semantic Representations of Symbolic Expressions

ICML 2017poster

Combining abstract, symbolic reasoning with continuous neural reasoning is a grand challenge of representation learning. As a step in this direction, we propose a new architecture, called neural equivalence network, for the problem of learning continuous semantic representations of algebraic and log…

Cited by 128SourcePDFScholar