Probabilistic Neural Programs

Kenton Murray, Jayant Krishnamurthy

Oct 14, 2016 (modified: Oct 14, 2016) NIPS 2016 workshop NAMPI submission readers: everyone
  • 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.
  • TL;DR: We present probabilistic neural programs, a framework for program induction using neural nets and probabilistic programming.
  • Conflicts: nd.ed, allenai.org, cmu.edu

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