UfM* uses Gaussian mixtures to compute multiview disagreement for uncertainty in depth estimation with single inference per image, reducing energy and memory use.
Accurate uncertainties for deep learning using calibrated regression
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Iterative receiver algorithms for PNC relays in multi-hop UWA networks achieve low BER of 10^{-5} in simulations and superior performance in lake and sea trials compared to baselines.
Diffusion sampler framework produces intrinsically calibrated predictive uncertainty for industrial soft sensors and process models via faithful posterior sampling.
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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Iterative Receiver Processing at Relays in PNC-Enabled Multi-Hop Underwater Acoustic Networks
Iterative receiver algorithms for PNC relays in multi-hop UWA networks achieve low BER of 10^{-5} in simulations and superior performance in lake and sea trials compared to baselines.
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Towards Intrinsically Calibrated Uncertainty Quantification in Industrial Data-Driven Models via Diffusion Sampler
Diffusion sampler framework produces intrinsically calibrated predictive uncertainty for industrial soft sensors and process models via faithful posterior sampling.