Interpretable ML for Science with PySR
Free while signed in. Answers cite the passages they came from.
First page
The curator’s take
Key pointsAn open-source library for practical symbolic regression in the sciences.
01
Distributed back-end: Built on a high-performance distributed back-end for scaling to larger scientific datasets.
02
DL integration: Interfaces with several deep learning packages so symbolic regression can be used alongside neural networks.
03
EmpiricalBench benchmark: Releases a new benchmark for quantifying the applicability of symbolic regression algorithms in science.
04
Science-AI tool: Became a widely-used tool for scientists seeking interpretable equations from data, complementing black-box DL.
Every Monday
Get next week’s papers.
The same picks and the same summaries, in your inbox. Free, and 176 issues deep.
Subscribe on Substack