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Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance Learning., , and . CoRR, (2023)Efficient remote sensing image classification with Gaussian processes and Fourier features., , , and . IGARSS, page 2227-2230. IEEE, (2017)A First Study on the Use of Boosting for Class Noise Reparation., , and . HAIS, volume 9648 of Lecture Notes in Computer Science, page 549-559. Springer, (2016)An end-to-end approach to combine attention feature extraction and Gaussian Process models for deep multiple instance learning in CT hemorrhage detection., , , , , , and . Expert Syst. Appl., (2024)Remote Sensing Image Classification with Large Scale Gaussian Processes., , , and . CoRR, (2017)Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO., , , , and . IEEE Trans. Pattern Anal. Mach. Intell., 44 (3): 1534-1551 (2022)Learning from crowds with variational Gaussian processes., , , and . Pattern Recognit., (2019)Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO., , , , and . CoRR, (2019)Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation., , and . ICCV, page 21040-21049. IEEE, (2023)Learning from crowds for automated histopathological image segmentation., , , , , , , and . Comput. Medical Imaging Graph., (2024)