Deep Ensembles
A method for estimating uncertainty by training multiple independently initialized models and aggregating their predictions. It effectively captures epistemic uncertainty and is simple to implement, but inference cost grows linearly with the number of models.
Timeline (last 90 days)
On synthetic microgrid trajectories, sampling-based generative heads (lightweight diffusion or flow-matching heads) achieved the best calibration, outperforming variational inference and Monte Carlo Dropout/deep ensembles.
Common implementation methods for uncertainty estimation include Monte Carlo Dropout, deep ensembles, and Bayesian neural networks.
All Facts (2)
On synthetic microgrid trajectories, sampling-based generative heads (lightweight diffusion or flow-matching heads) achieved the best calibration, outperforming variational inference and Monte Carlo Dropout/deep ensembles.
50%UnverifiedCommon implementation methods for uncertainty estimation include Monte Carlo Dropout, deep ensembles, and Bayesian neural networks.
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