← Mathematical compendium

Published equation contexts

ϕ(f,x)\phi(f, x)

Why this formula appears here

This kind of benchmark descends from an older and more basic validation move: check whether an explanation changes when it should have to. Adebayo and colleagues showed that several popular saliency methods for image classifiers produce visually similar output whether the underlying model has been trained at all or left at its random initialization, meaning the “explanation” was substantially independent of what training had actually done [ 6 ] . Stated as a minimal necessary condition, if θ\theta are a trained model’s weights, θrand\theta_{\mathrm{rand}} the same architecture reinitialized at random, and ϕ(f,x)\phi(f, x) the explanation a method produces for input x under model f , then a method worth…

Read the full article-specific guide →

Read the representative guide

ϕ\phi

Symbol phi

phi is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

Read this term in its guide →
ff

Symbol f

f is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

Read this term in its guide →
xx

Symbol x

x is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

Read this term in its guide →

How to interpret it

Read this expression with the definitions, units, and assumptions supplied by the article.

Research cited beside this formula

Published contexts (1)

A symbol can carry a different meaning in another article. Each occurrence keeps its own guide and term definitions.

ϕ(f,x)\phi(f, x)

Equation 3 · AI Research

How Do We Know an Interpretability Claim Is Actually Right?

This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text.

This kind of benchmark descends from an older and more basic validation move: check whether an explanation changes when it should have to. Adebayo and colleagues showed that several popular saliency methods for image classifiers produce visually similar output whether the underlying model has been trained at all or left at its random initialization, meaning the “explanation” was substantially independent of what training had actually done [ 6 ] . Stated as a minimal necessary condition, if θ\theta are a trained model’s weights, θrand\theta_{\mathrm{rand}} the same architecture reinitialized at random, and ϕ(f,x)\phi(f, x) the explanation a method produces for input x under model f , then a method worth…

Equation guide → · Article →