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.
@article{arxiv.2206.05922,
title = {From Perception to Programs: Regularize, Overparameterize, and Amortize},
author = {Hao Tang and Kevin Ellis},
journal= {arXiv preprint arXiv:2206.05922},
year = {2023}
}