Probabilistic Neural Programs

Kenton W. Murray; Jayant Krishnamurthy

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arXiv · 1612.00712

Probabilistic Neural Programs

Kenton W. Murray·Jayant Krishnamurthy

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Abstract

We present probabilistic neural programs, a framework for program induction that permits flexible specification of both a computational model and inference algorithm while simultaneously enabling the use of deep neural networks. Probabilistic neural programs combine a computation graph for specifying a neural network with an operator for weighted nondeterministic choice. Thus, a program describes both a collection of decisions as well as the neural network architecture used to make each one. We evaluate our approach on a challenging diagram question answering task where probabilistic neural programs correctly execute nearly twice as many programs as a baseline model.

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Kenton W. Murray, Jayant Krishnamurthy. 2016-12-02. Probabilistic Neural Programs. https://arxiv.org/abs/1612.00712

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