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Learning Neurosymbolic Generative Models via Program Synthesis
[article]
2019
arXiv
pre-print
Significant strides have been made toward designing better generative models in recent years. Despite this progress, however, state-of-the-art approaches are still largely unable to capture complex global structure in data. For example, images of buildings typically contain spatial patterns such as windows repeating at regular intervals; state-of-the-art generative methods can't easily reproduce these structures. We propose to address this problem by incorporating programs representing global
arXiv:1901.08565v1
fatcat:kz773zueh5colixcoiua4lidji