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

NeurIPS 2021 · Probabilistic and scientific modeling › Dynamical systems · approach: Latent-space control and planning · 9 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use BSP symbolic regression instead of neural force/dynamics modelsBSP · SYNTH NBODY/BOUNCE/MATpredictive nRMSE vs training scenesHelped
use BSP instead of zero-force baseline with latent propertiesBSP EM · NBODY partially observednRMSE: 1.77e0 -> 8.05e-1Helped
use BSP instead of zero-force baselineBSP · Physics101 FALLnRMSE: 5.05e-2 -> 4.30e-2Helped
use BSP instead of zero-force baselineBSP · Physics101 SPRINGnRMSE: 2.19e-2 -> 9.22e-3Helped
add dimensional analysis prior to the grammar (ablation)BSP grammar · SYNTH NBODY/BOUNCEnRMSE vs training scenes: 7,935,408 -> 75,816 expressions at depth 5Helped
add translation invariance prior to the grammar (ablation)BSP grammar · SYNTH NBODY/BOUNCEnRMSE vs training scenes: 8,593,200 -> 7,935,408 expressions at depth 5Helped
add physics priors to the grammar (ablation)BSP grammar · SYNTH MATnRMSENo clear effect
learn constants via bilevel L-BFGSBSP · force law searchsearch efficiencyHelped
use BSP instead of human participantsBSP · ULLMAN questionsaccuracy: 68% -> 55% (Global)Hurt
use BSP on BOUNCEBSP · BOUNCEforce law recoveryHurt

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