From Perception to Programs: Regularize, Overparameterize, and Amortize

• URL: http://arxiv.org/abs/2206.05922v2
• Date: Wed, 31 May 2023 19:10:41 GMT
• Title: From Perception to Programs: Regularize, Overparameterize, and Amortize
• Authors: Hao Tang and Kevin Ellis
• Abstract summary: We develop techniques for neurosymbolic program synthesis where perceptual input is first parsed by neural nets into a low-dimensional interpretable representation, which is then processed by a synthesized program. We explore several techniques for relaxing the problem and jointly learning all modules end-to-end with gradient descent. Collectedly this toolbox improves the stability of gradient-guided program search, and suggests ways of learning both how to perceive input as discrete abstractions, and how to symbolically process those abstractions as programs.
• Score: 21.221244694737134
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Toward combining inductive reasoning with perception abilities, we develop techniques for neurosymbolic program synthesis where perceptual input is first parsed by neural nets into a low-dimensional interpretable representation, which is then processed by a synthesized program. We explore several techniques for relaxing the problem and jointly learning all modules end-to-end with gradient descent: multitask learning; amortized inference; overparameterization; and a differentiable strategy for penalizing lengthy programs. Collectedly this toolbox improves the stability of gradient-guided program search, and suggests ways of learning both how to perceive input as discrete abstractions, and how to symbolically process those abstractions as programs.

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