MC dropout: variational uncertainty from stochastic forward passes

What changed

Interprets dropout networks through an approximate variational Bayesian model and estimates predictive moments by retaining random dropout masks during repeated inference passes.

Scientific scope

Interprets dropout networks through an approximate variational Bayesian model and estimates predictive moments by retaining random dropout masks during repeated inference passes. Not invention of dropout or model averaging, nor exact Bayesian posterior inference. The approximation depends on model, likelihood, regularization and dropout choices; T forward passes still incur T-sample compute despite possible parallel latency. No universal uncertainty-calibration guarantee.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Not invention of dropout or model averaging, nor exact Bayesian posterior inference. The approximation depends on model, likelihood, regularization and dropout choices; T forward passes still incur T-sample compute despite possible parallel latency. No universal uncertainty-calibration guarantee.

Contributors

• Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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