Equation 28 · Shrink It, Train It Small, or Search for It: The Main Strategies for Small Models, Compared
What does this equation mean?
Read the formula alongside the article passage below. Each part has a deeper page with its role in the equation, the supporting passage and nearby citations.
This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
Read it piece by piece
Symbol E
E is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.
How to interpret it
Read this expression with the definitions, units, and assumptions supplied by the article.
What the article says around this equation
Shazeer and colleagues stated the underlying argument for conditional computation directly: “a trainable gating network determines a sparse combination of experts to use for each example,” a mechanism they showed could scale model capacity by “over 1000x” while keeping the compute spent on any one example roughly constant [ 11 ] . The now-standard form of a sparse mixture-of-experts layer routes each token to a small top- k subset of E available experts:
Sources cited in the surrounding passage
These citations give research context. Read each source to check which claims it supports.