12 citations · 21 across the 4 of their papers we have counts for
4 papers
Nonparametric Feature Impact and Importance
Terence Parr, James D. Wilson, Jeff Hamrick
Practitioners use feature importance to rank and eliminate weak predictors during model development in an effort to simplify models and improve generality. Unfortunately, they also…
Technical Report: Partial Dependence through Stratification
Terence Parr, James D. Wilson
Partial dependence curves (FPD) introduced by Friedman, are an important model interpretation tool, but are often not accessible to business analysts and scientists who typically l…
The Matrix Calculus You Need For Deep Learning
Terence Parr, Jeremy Howard
This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no math knowledge beyond what you learne…
Technical Report: Towards a Universal Code Formatter through Machine Learning
Terence Parr, Jurgin Vinju
There are many declarative frameworks that allow us to implement code formatters relatively easily for any specific language, but constructing them is cumbersome. The first problem…