Neural Functional Programming
نویسندگان
چکیده
We discuss a range of modeling choices that arise when constructing an end-to-end differentiable programming language suitable for learning programs from inputoutput examples. Taking cues from programming languages research, we study the effect of memory allocation schemes, immutable data, type systems, and builtin control-flow structures on the success rate of learning algorithms. We build a range of models leading up to a simple differentiable functional programming language. Our empirical evaluation shows that this language allows to learn far more programs than existing baselines.
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عنوان ژورنال:
- CoRR
دوره abs/1611.01988 شماره
صفحات -
تاریخ انتشار 2016