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From Perception to Programs: Regularize, Overparameterize, and Amortize

2022-06-13 06:27:11
Hao Tang, Kevin Ellis
arXiv_AI
       
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.

Abstract (translated)

URL

https://arxiv.org/abs/2206.05922

PDF

https://arxiv.org/pdf/2206.05922.pdf

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