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Equation 5 · Part 10 · The Hardest Unsolved Problems in Small and On-Device AI

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Δˉ=1T∑t=1T(AcctFP−AcctQ)\bar{\Delta} = \frac{1}{T}\sum_{t=1}^{T}\left(\mathrm{Acc}_t^{FP} - \mathrm{Acc}_t^{Q}\right)
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What this part means

A raised number can be a power. When it is a label or bound, it selects a case or the upper limit of a sum; the formula’s structure distinguishes these uses.

Its job in the formula

A raised mark can be a power or an index. Its position and the surrounding notation determine which.

The passage around this formula

Formally, if a model is evaluated on T tasks with full-precision accuracy AcctFP\mathrm{Acc}_t^{FP} and quantized accuracy AcctQ\mathrm{Acc}_t^{Q} on task t , a benchmark table typically reports the mean regression Δˉ=1T∑t=1T(AcctFP−AcctQ)\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

An exponent tells how a base is used in multiplication. In x³, x is the base and 3 is the exponent: x³ = x × x × x.

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Sources cited in the article section

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