Dirichlet-Based Monte Carlo Dropout for Uncertainty Estimation in Neural Networks
Traditional neural networks provide deterministic predictions without inherent uncertainty estimates. While Bayesian Neural Networks (BNNs) offer a principled approach to uncertainty quantification, their computational complexity limits scalability. Monte Carlo (MC) Dropout, initially introduced as a regularization technique, has been shown to approximate Bayesian inference by enabling probabilistic modeling through multiple stochastic forward passes. In this work, we enhance uncertainty estimation in deep learning by integrating a Dirichlet-based framework within MC Dropout. Specifically, we leverage the formulation proposed by Sensoy et al. (2018), where class probabilities are modeled ...
| Authors | Rouaa Hoblos, Noura Dridi, Noureddine Zerhouni et al. |
|---|---|
| Primary category | stat.ML |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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