Description
Chair: Laurence Perreault Levasseur
I will demonstrate a framework for interpretable machine learning, using physically-motivated inductive biases and a technique we have termed “symbolic distillation”. This method allows a practitioner to translate a trained neural network model into an interpretable symbolic expression via the use of symbolic regression with a basis set of operators. I will first discuss the deep learning strategy for performing this distillation, and then review “symbolic regression,” an algorithm for optimizing symbolic expressions using evolutionary algorithms. In particular, I will describe the PySR/SymbolicRegression.jl software framework (github.com/MilesCranmer/PySR), which is an easy-to-use high-performance symbolic regression package in Python and Julia. Tangential to this, I will discuss several physically-motivated inductive biases which make this technique more effective. In the second half of this talk, I will review a variety of applications of this and other interpretable machine learning techniques, focusing on a few problems in astrophysics.
