Equation 5 · Part 3 · The Hardest Unsolved Problems in Small and On-Device AI

Symbol t

\bar{\Delta} = \frac{1}{T}\sum_{t=1}^{T}\left(\mathrm{Acc}_t^{FP} - \mathrm{Acc}_t^{Q}\right)

t

What this part means

t appears in the bound of this sum. The bound states where the repeated operation starts, ends, or which values it includes.

Its job in the formula

t appears in the bound of this sum. The bound states where the repeated operation starts, ends, or which values it includes.

Full expressionSymbol tArticle meaning

The passage around this formula

Formally, if a model is evaluated on T tasks with full-precision accuracy $\mathrm{Acc}_t^{FP}$ and quantized accuracy $\mathrm{Acc}_t^{Q}$ on task t , a benchmark table typically reports the mean regression $\bar{\Delta} = \frac{1}{T}\sum_{t=1}^{T}\left(\mathrm{Acc}_t^{FP} - \mathrm{Acc}_t^{Q}\right)$ . while what governs whether any individual deployment is safe to ship is closer to the worst-case regression

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Learn the underlying idea

A function assigns an output to each allowed input. The expression f(x) means “apply f to x”.

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See this notation across published equations →

Sources cited in the article section

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