Max Tegmark, MIT
Abstract: After briefly reviewing how machine learning is becoming ever-more widely used in physics, I explore how ideas and methods from physics can help improve machine learning, focusing on automated discovery of mathematical formulas from data. I present a method for unsupervised learning of equations of motion for objects in raw and optionally distorted unlabeled video. I also describe progress on symbolic regression, i.e., finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in general, functions of practical interest often exhibit symmetries, separability, compositionality and other simplifying properties. In this spirit, we have developed a recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques that discover and exploit these simplifying properties, enabling significant improvement of state-of-the-art performance.
Related papers:
* AI Feynman: a Physics-Inspired Method for Symbolic Regression - https://arxiv.org/abs/1905.11481
* AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity - https://arxiv.org/abs/2006.10782
* Symbolic Pregression: Discovering Physical Laws from Raw Distorted Video - https://arxiv.org/abs/2005.11212