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Found 4 papers, 4 papers with code
Nonparametric Feature Impact and Importance
In this paper, we give mathematical definitions of feature impact and importance, derived from partial dependence curves, that operate directly on the data.
Technical Report: Partial Dependence through Stratification
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 lack the skills to choose, tune, and assess machine learning models.
The Matrix Calculus You Need For Deep Learning
This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks.
Technical Report: Towards a Universal Code Formatter through Machine Learning
There are many declarative frameworks that allow us to implement code formatters relatively easily for any specific language, but constructing them is cumbersome.
