# Improving Predictive Uncertainty Estimation using Dropout -- Hamiltonian Monte Carlo Source: [Improving Predictive Uncertainty Estimation using Dropout -- Hamiltonian Monte Carlo]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:40:30.056Z Authors: Diego Vergara, Sergio Hern\\'andez, Matias Valdenegro-Toro, Felipe Jorquera Published: 2018-05-12T18:11:33.000Z ## Abstract Estimating predictive uncertainty is crucial for many computer vision tasks, from image classification to autonomous driving systems. Hamiltonian Monte Carlo (HMC) is an sampling method for performing Bayesian inference. On the other hand, Dropout regularization has been proposed as an approximate model averaging technique that tends to improve generalization in large scale models such as deep neural networks. Although, HMC provides convergence guarantees for most standard Bayesian models, it does not handle discrete parameters arising from Dropout regularization. In this paper, we present a robust methodology for improving predictive uncertainty in classification problems, based on Dropout and Hamiltonian Monte Carlo. Even though Dropout induces a non-smooth energy function with no such convergence guarantees, the resulting discretization of the Hamiltonian proves empirical success. The proposed method allows to effectively estimate the predictive accuracy and to provide better generalization for difficult test examples. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()