PriUS enforces uncertainty estimates in segmentation models via evidential learning to match image contrast, corruption levels, and shape complexity, yielding more consistent uncertainty on ACDC, ISIC, and WHS datasets while preserving segmentation accuracy.
What uncertainties do we need in bayesian deep learning for computer vision?
3 Pith papers cite this work. Polarity classification is still indexing.
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VLMaterial fuses VLMs and physics-based radar analysis via PRCA extraction and context-augmented generation to reach 96.08% material identification accuracy on 41 everyday objects without task-specific training.
Derives a posterior-predictive variance decomposition separating epistemic and aleatoric uncertainty in heteroscedastic Bayesian neural network models for wind power forecasting, with a dedicated validation framework tested on synthetic and real SCADA data.
citing papers explorer
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Principle-Guided Supervision for Interpretable Uncertainty in Medical Image Segmentation
PriUS enforces uncertainty estimates in segmentation models via evidential learning to match image contrast, corruption levels, and shape complexity, yielding more consistent uncertainty on ACDC, ISIC, and WHS datasets while preserving segmentation accuracy.
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VLMaterial: Vision-Language Model-Based Camera-Radar Fusion for Physics-Grounded Material Identification
VLMaterial fuses VLMs and physics-based radar analysis via PRCA extraction and context-augmented generation to reach 96.08% material identification accuracy on 41 everyday objects without task-specific training.
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A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting
Derives a posterior-predictive variance decomposition separating epistemic and aleatoric uncertainty in heteroscedastic Bayesian neural network models for wind power forecasting, with a dedicated validation framework tested on synthetic and real SCADA data.