AI RESEARCH
Monte Carlo Stochastic Depth for Uncertainty Estimation in Deep Learning
arXiv CS.LG
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ArXi:2604.12719v1 Announce Type: new The deployment of deep neural networks in safety-critical systems necessitates reliable and efficient uncertainty quantification (UQ). A practical and widespread strategy for UQ is repurposing stochastic regularizers as scalable approximate Bayesian inference methods, such as Monte Carlo Dropout (MCD) and MC-DropBlock (MCDB). However, this paradigm remains under-explored for Stochastic Depth (SD), a regularizer integral to the residual-based backbones of most modern architectures.