The Importance of Being Interpretable
Cosmology is entering an era of data-driven science, due in part to modern machine learning techniques that enable powerful new data analysis methods. This is a shift in our scientific approach, and requires us to ask an important question: Can we trust the black box? In this talk, I will describe methods for building trust in machine learning models, focusing on models for interpreting cosmological large scale structure. I will show examples of how machine learning can be used, not just as a tool for getting ?better? results at the expense of understanding, but as a partner that can point us toward physical discovery.
|Date: ||Jeudi, le 27 janvier 2022|
|Lieu: ||Université de Montréal|