Mar 11, 2024 · To mitigate the adverse impact of predictive uncertainty on Gnn predictions, it is crucial to systematically identify, quantify, and utilize uncertainty.

Mar 29, 2024 · In this paper, we explore a simplified method for quantifying both aleatoric and epistemic uncertainty in deterministic networks, called SAEU. In SAEU, ...

Mar 28, 2024 · In this work, we propose a method called Uncertainty-aware Pseudo-label-filtering Adaptation (UPA) to efficiently address this issue in a coarse-to-fine manner.

Jan 18, 2024 · We aim to enable direct learning on clean samples while leveraging the robustness of NT against noise in a unified framework. To mitigate the abundance of noisy ...

Feb 17, 2024 · Learning with noisy labels has gained increasing attention because the inevitable imperfect labels in real-world scenarios can substantially hurt the deep ...

Mar 6, 2024 · This study introduces an uncertainty-aware, graph deep learning model that predicts endovascular thrombectomy outcomes using clinical features and imaging ...

Sep 22, 2024 · Uncertainty aware semi-supervised learning on graph data, PDF · GitHub stars. 2 ... Interpretable self-aware neural networks for robust trajectory prediction ...

Dec 18, 2023 · We propose a learning-based method to efficiently tackle this problem without relying on a map of the environment or the robot's position.

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Oct 8, 2024 · Accurate uncertainty estimations are essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator ...