Correcting the Dropout-LayerNorm Expectation Gap Improves Protein Structure Models
Although $\mathbb{E}[\mathrm{dropout}(x)] = x$, here we show that $\mathbb{E}[\mathrm{LayerNorm}(\mathrm{dropout}(x))]$ is not equal to $\mathrm{LayerNorm}(x)$. Accordingly, the pattern of a Dropout layer followed by a LayerNorm, which is common to many AlphaFold2-based protein structure predictors, produces a systematic bias at evaluation time that can hamper performance. To address this, we derive a closed-form, first-order correction for this gap, which we call a Dropout-LayerNorm Correction (DLC). DLC empirically matches the performance boost of large Monte Carlo dropout ensembles. We evaluate its effect across nine protein structure models (ESMFold, OpenFold, ABB3, FlashABB, Ibex, Nb...
| Authors | Isaac Ellmen, David Errington, Matthew I. J. Raybould et al. |
|---|---|
| Primary category | q-bio.BM |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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