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Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networks. (arXiv:2202.11132v1 [cs.LG])
Feb. 24, 2022, 2:11 a.m. | Soumyasundar Pal, Antonios Valkanas, Florence Regol, Mark Coates
cs.LG updates on arXiv.org arxiv.org
Multiple Instance Learning (MIL) is a weakly supervised learning problem
where the aim is to assign labels to sets or bags of instances, as opposed to
traditional supervised learning where each instance is assumed to be
independent and identically distributed (IID) and is to be labeled
individually. Recent work has shown promising results for neural network models
in the MIL setting. Instead of focusing on each instance, these models are
trained in an end-to-end fashion to learn effective bag-level representations …
arxiv bag bayesian graph graph neural networks learning networks neural networks
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