Symbol barΔ
barΔ is part of the quantity the equation computes from the expression on the right.
Read this term in its guide →Published equation contexts
Why this formula appears here
Formally, if a model is evaluated on T tasks with full-precision accuracy and quantized accuracy on task t , a benchmark table typically reports the mean regression . while what governs whether any individual deployment is safe to ship is closer to the worst-case regression
Read the representative guide
Symbol T
T occurs below the fraction bar. The quantity above the bar is divided by this expression; zero is excluded as a denominator.
Read this term in its guide →Symbol t
t appears in the bound of this sum. The bound states where the repeated operation starts, ends, or which values it includes.
Read this term in its guide →Symbol t^FP
P is one of the signed contributions combined to compute the quantity on the left.
Read this term in its guide →Symbol t^Q
is one of the signed contributions combined to compute the quantity on the left.
Read this term in its guide →Starting index or lower bound: t=1
This label says where the repeated addition, multiplication, or accumulation starts. Read its value or condition together with the article’s description of the index.
Read this term in its guide →Ending index or upper bound: T
This label says where the repeated addition, multiplication, or accumulation stops. It sets the last term or end of the range.
Read this term in its guide →How to interpret it
With a fixed numerator, increasing a nonzero denominator reduces the fraction. Read it 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.
Equation 5 · Edge AI & Electronics
The Hardest Unsolved Problems in Small and On-Device AI
This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.
Formally, if a model is evaluated on T tasks with full-precision accuracy and quantized accuracy on task t , a benchmark table typically reports the mean regression . while what governs whether any individual deployment is safe to ship is closer to the worst-case regression
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