Explainability in Neural Networks, Part 4: Path Methods for Feature Attribution
This post will delve deeper into Path Integrated Gradient Methods for Feature Attribution in Neural Networks.…
Explainability in Neural Networks, Part 3: The Axioms of Attribution
In this third post of the series on Explainability in Neural Networks, we present Axioms of Attribution, which are a set of desirable properties that any reasonable feature-attribution method should have.…
Explainability in Neural Networks, Part 2: Limitations of Simple Feature Attribution Methods
We examine some simple, intuitive methods to explain the output of a neural network (based on perturbations and gradients), and see how they produce non-sensical results for non-linear functions.…
Explainability in Deep Neural Networks
The wild success of Deep Neural Network (DNN) models in a variety of domains has created considerable excitement in the machine learning community. Despite this success, a deep understanding of why DNNs perform so well, and whether their performance is somehow brittle, has been lacking.…