Symbolic Pregression: Discovering Physical Laws from Distorted Video

19 May 2020

Papers citing "Symbolic Pregression: Discovering Physical Laws from Distorted Video"

7 / 7 papers shown

Title
Grokking as Compression: A Nonlinear Complexity Perspective Ziming Liu Ziqian Zhong Max Tegmark 38 9 0 09 Oct 2023
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers Stéphane d’Ascoli Soren Becker Alexander Mathis Philippe Schwaller Niki Kilbertus 26 21 0 09 Oct 2023
Quantifying Complexity: An Object-Relations Approach to Complex Systems S. Casey 15 1 0 22 Oct 2022
End-to-end symbolic regression with transformers Pierre-Alexandre Kamienny Stéphane dÁscoli Guillaume Lample Franccois Charton 25 163 0 22 Apr 2022
Noether Networks: Meta-Learning Useful Conserved Quantities Ferran Alet Dylan D. Doblar Allan Zhou J. Tenenbaum Kenji Kawaguchi Chelsea Finn 73 26 0 06 Dec 2021
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision I. Higgins Peter Wirnsberger Andrew Jaegle Aleksandar Botev 44 8 0 10 Nov 2021
A Compositional Object-Based Approach to Learning Physical Dynamics Michael Chang T. Ullman Antonio Torralba J. Tenenbaum AI4CE OCL 241 438 0 01 Dec 2016