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A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data.

, , , , , and . Remote. Sens., 13 (2): 287 (2021)

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Efficient Adaptive Multiple Importance Sampling., , , and . EUSIPCO, page 1-5. IEEE, (2019)Multioutput Feature Selection for Emulation and Sensitivity Analysis., , , , and . IEEE Trans. Geosci. Remote. Sens., (2024)Automatic tempered posterior distributions for Bayesian inversion problems., , , , and . CoRR, (2021)Adaptive Importance Sampling: The past, the present, and the future., , , , , and . IEEE Signal Process. Mag., 34 (4): 60-79 (2017)Sticky proposal densities for adaptive MCMC methods., , and . SSP, page 1-5. IEEE, (2016)Probabilistic cross-validation estimators for Gaussian process regression., , and . EUSIPCO, page 823-827. IEEE, (2017)Physics-Aware Gaussian Processes for Earth Observation., , , , , , and . SCIA (2), volume 10270 of Lecture Notes in Computer Science, page 205-217. Springer, (2017)Joint Gaussian processes for inverse modeling., , , and . IGARSS, page 3980-3983. IEEE, (2017)Efficient monte carlo optimization for multi-label classifier chains., , and . ICASSP, page 3457-3461. IEEE, (2013)Importance Gaussian Quadrature, , and . (2020)cite arxiv:2001.03090.