A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics

no code implementations • NeurIPS 2021 • Kai Xu, Akash Srivastava, Dan Gutfreund, Felix Sosa, Tomer Ullman, Josh Tenenbaum, Charles Sutton

In this paper, we propose a Bayesian-symbolic framework (BSP) for physical reasoning and learning that is close to human-level sample-efficiency and accuracy.

Bayesian Inference Bilevel Optimization +3

A Bayesian-Symbolic Approach to Learning and Reasoning for Intuitive Physics

no code implementations • 1 Jan 2021 • Kai Xu, Akash Srivastava, Dan Gutfreund, Felix Sosa, Tomer Ullman, Joshua B. Tenenbaum, Charles Sutton

As such, learning the laws is then reduced to symbolic regression and Bayesian inference methods are used to obtain the distribution of unobserved properties.

Bayesian Inference Common Sense Reasoning +2

Write, Execute, Assess: Program Synthesis with a REPL

no code implementations • NeurIPS 2019 • Kevin Ellis, Maxwell Nye, Yewen Pu, Felix Sosa, Josh Tenenbaum, Armando Solar-Lezama

We present a neural program synthesis approach integrating components which write, execute, and assess code to navigate the search space of possible programs.

Navigate Program Synthesis