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Representing QoT model uncertainty with deep quantile inference is in essence based on learning conditional quantile functions with DNNs that are trained by means of regression.
In this work, we present and compare two uncertainty representation frameworks for deep QoT estimation models.
In this work, we present and compare two uncertainty representation frameworks for deep QoT estimation models.
Jul 5, 2022 · Specifically, deep quantile regression for QoT estimation ensures that lightpaths with insufficient QoT will be accurately identified and ...
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Representing Uncertainty in Deep QoT Models · List of references · Publications that cite this publication.
Jul 20, 2022 · The output of the QoT model is an estimate or an approximation containing some uncertainty that is represented by the variability around the ...
Feb 10, 2023 · Abstract. Reliable uncertainty quantification on RUL prediction is crucial for informative decision-making in predictive maintenance.
Uncertainty-aware Deep Learning with SNGP | TensorFlow Core
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Apr 3, 2024 · SNGP is a simple approach to improve a deep classifier's uncertainty quality while maintaining a similar level of accuracy and latency.
Jun 4, 2020 · Furthermore, we looked at how all models have uncertainty within them but not all models can calculate uncertainty because of the non-linearity.
Apr 2, 2022 · Parametric uncertainty can usually be represented by a possibility set, which is typically presented as a numerical interval. In some cases, a ...