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Found 5 papers, 3 papers with code
Uncertainty-Aware Guidance for Target Tracking subject to Intermittent Measurements using Motion Model Learning
This neural network (NN)-based motion model serves as the prediction step in a particle filter for target state estimation and uncertainty quantification.
DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability
OOD data consists of test data that is significantly different from the model's training data.
DOMINO: Domain-aware Loss for Deep Learning Calibration
Deep learning has achieved the state-of-the-art performance across medical imaging tasks; however, model calibration is often not considered.
DOMINO: Domain-aware Model Calibration in Medical Image Segmentation
Our experiments demonstrate that our DOMINO-calibrated deep neural networks outperform non-calibrated models and state-of-the-art morphometric methods in head image segmentation.
Information-Aware Guidance for Magnetic Anomaly based Navigation
In the absence of an absolute positioning system, such as GPS, autonomous vehicles are subject to accumulation of positional error which can interfere with reliable performance.
