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Δmax⁡=max⁡t(AcctFP−AcctQ)\Delta_{\max} = \max_{t}\left(\mathrm{Acc}_t^{FP} - \mathrm{Acc}_t^{Q}\right)

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while what governs whether any individual deployment is safe to ship is closer to the worst-case regression Δmax⁡=max⁡t(AcctFP−AcctQ)\Delta_{\max} = \max_{t}\left(\mathrm{Acc}_t^{FP} - \mathrm{Acc}_t^{Q}\right). A quantization scheme can post a Δˉ\bar{\Delta} close to zero while Δmax⁡\Delta_{\max} is large, provided the loss concentrates on one or a few tasks that are a small share of the suite. Nothing about Δˉ\bar{\Delta} being small implies Δmax⁡\Delta_{\max} is small; the two only converge if degradation is spread evenly, and the studies below find that it is not.

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Δmax⁡\Delta_{\max}

Symbol Delta_max

Deltama_max is part of the quantity the equation computes from the expression on the right.

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Published contexts (1)

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Δmax⁡=max⁡t(AcctFP−AcctQ).\Delta_{\max} = \max_{t}\left(\mathrm{Acc}_t^{FP} - \mathrm{Acc}_t^{Q}\right).

Equation 6 · 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.

while what governs whether any individual deployment is safe to ship is closer to the worst-case regression Δmax⁡=max⁡t(AcctFP−AcctQ)\Delta_{\max} = \max_{t}\left(\mathrm{Acc}_t^{FP} - \mathrm{Acc}_t^{Q}\right). A quantization scheme can post a Δˉ\bar{\Delta} close to zero while Δmax⁡\Delta_{\max} is large, provided the loss concentrates on one or a few tasks that are a small share of the suite. Nothing about Δˉ\bar{\Delta} being small implies Δmax⁡\Delta_{\max} is small; the two only converge if degradation is spread evenly, and the studies below find that it is not.

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