Reading Group: Blog
[internal] Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
With an increasing demand for explaining black-box models, many different interpretability methods have developed. One of these methods is the Concept Activation Vector (CAV) which is a concept-based approach providing an interpretation of the...
[internal] Iterative Tweaking and the Interpretation of Feature Visualization
1. Feature Visualization One way to make progress in rendering deep artificial neural networks intelligible is to figure out what is going on inside their hidden layers. ‘Hidden’ in this context just means located between the input and output layers1. Drawing...
[internal] LIME Without The Math
LIME (which is an acronym for Local Interpretable Model-Agnostic Explanations) is one of the most cited techniques in the Explainable Artificial Intelligence (XAI) debate (according to Google Scholar, it was cited around 5.5k times). Indeed, the whole (renewed) XAI...