AI RESEARCH
Dirichlet-Based Monte Carlo Dropout for Uncertainty Estimation in Neural Networks
arXiv CS.LG
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ArXi:2605.23635v1 Announce Type: cross 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