UfM* uses Gaussian mixtures to compute multiview disagreement for uncertainty in depth estimation with single inference per image, reducing energy and memory use.
Towards inference efficient deep ensemble learning
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Meta-ensemble learning on diverse ICBHI data splits reaches 66.49% Score and improves generalization on two external datasets.
citing papers explorer
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UfM*: Uncertainty from Motion* for DNN Depth Estimation Using Gaussians
UfM* uses Gaussian mixtures to compute multiview disagreement for uncertainty in depth estimation with single inference per image, reducing energy and memory use.
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Meta-Ensemble Learning with Diverse Data Splits for Improved Respiratory Sound Classification
Meta-ensemble learning on diverse ICBHI data splits reaches 66.49% Score and improves generalization on two external datasets.