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- W4387664891 abstract "<p>This paper highlights the contribution of utilizing ensemble deep learning with auto-encoders (AEs) for out-of-distribution data detection. The key innovation is treating ensemble UQ as a regression problem, mapping uncertainty distribution to a single model, reducing computational demands. This approach aligns well with the ensemble of AEs' uncertainty distribution, making it valuable for resource-constrained systems and rapid decision-making in computational intelligence.</p>" @default.
- W4387664891 created "2023-10-17" @default.
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- W4387664891 date "2023-10-16" @default.
- W4387664891 modified "2023-10-17" @default.
- W4387664891 title "Streamlined and Resource-Efficient Predictive Uncertainty Estimation of Deep Ensemble Predictions via Regression" @default.
- W4387664891 doi "https://doi.org/10.36227/techrxiv.24291208" @default.
- W4387664891 hasPublicationYear "2023" @default.
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