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Quantifying Uncertainty in Machine Learning Based Soil Moisture Retrieval From GNSS-R Measurements In Proceedings

2024 International Conference on Electromagnetics in Advanced Applications (ICEAA), Lisbon, Portugal, 2024, pp. 492-492, doi: 10.1109/ICEAA61917.2024.10701691.

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Quantifying the Predictive Capacity of Dynamic Graph Measures on Systemic and Tail Risk Journal Articles

Tzagkarakis George et al.

Computational Economics.

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Quantifying the structure of strong gravitational lens potentials with uncertainty-aware deep neural networks Journal Articles

Tsagkatakis Grigorios

Monthly Notices of the Royal Astronomical Society, 499 (4), pp. 5641–5652, 2020.

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Publications

Quantifying Uncertainty in Machine Learning Based Soil Moisture Retrieval From GNSS-R Measurements In Proceedings

Quantifying the Predictive Capacity of Dynamic Graph Measures on Systemic and Tail Risk Journal Articles

Quantifying the structure of strong gravitational lens potentials with uncertainty-aware deep neural networks Journal Articles

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