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arXiv · 1906.07304

Neurally-Guided Structure Inference

Sidi Lu·Jiayuan Mao·Joshua B. Tenenbaum·Jiajun Wu
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Abstract

Most structure inference methods either rely on exhaustive search or are purely data-driven. Exhaustive search robustly infers the structure of arbitrarily complex data, but it is slow. Data-driven methods allow efficient inference, but do not generalize when test data have more complex structures than training data. In this paper, we propose a hybrid inference algorithm, the Neurally-Guided Structure Inference (NG-SI), keeping the advantages of both search-based and data-driven methods. The key idea of NG-SI is to use a neural network to guide the hierarchical, layer-wise search over the compositional space of structures. We evaluate our algorithm on two representative structure inference tasks: probabilistic matrix decomposition and symbolic program parsing. It outperforms data-driven and search-based alternatives on both tasks.

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Sidi Lu, Jiayuan Mao, Joshua B. Tenenbaum, Jiajun Wu. 2019-06-17. Neurally-Guided Structure Inference. https://arxiv.org/abs/1906.07304

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