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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 networks, for the problem of learning continuous semantic representations of algebraic and logical expressions. These networks are trained to represent semantic equivalence, even of expressions that are syntactically very different. The challenge is that semantic representations must be computed in a syntax-directed manner, because semantics is compositional, but at the same time, small changes in syntax can lead to very large changes in semantics, which can be difficult for continuous neural architectures. We perform an exhaustive evaluation on the task of checking equivalence on a highly diverse class of symbolic algebraic and boolean expression types, showing that our model significantly outperforms existing architectures.
|Title of host publication||The 34th International Conference on Machine Learning (ICML 2017)|
|Place of Publication||Sydney, Australia|
|Number of pages||9|
|Publication status||Published - 11 Aug 2017|
|Event||34th International Conference on Machine Learning (ICML), 2017 - Sydney, Australia|
Duration: 6 Aug 2017 → 11 Aug 2017
|Name||Proceedings of Machine Learning Research|
|Conference||34th International Conference on Machine Learning (ICML), 2017|
|Period||6/08/17 → 11/08/17|
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