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Depth Uncertainty Networks for Active Learning. (arXiv:2112.06796v2 [cs.LG] UPDATED)
May 5, 2022, 1:12 a.m. | Chelsea Murray, James U. Allingham, Javier Antorán, José Miguel Hernández-Lobato
cs.LG updates on arXiv.org arxiv.org
In active learning, the size and complexity of the training dataset changes
over time. Simple models that are well specified by the amount of data
available at the start of active learning might suffer from bias as more points
are actively sampled. Flexible models that might be well suited to the full
dataset can suffer from overfitting towards the start of active learning. We
tackle this problem using Depth Uncertainty Networks (DUNs), a BNN variant in
which the depth of …
More from arxiv.org / cs.LG updates on arXiv.org
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