A small BERT model trained on only 117 of 517 curated regulatory relationships selected as confident errors reaches 93% balanced accuracy, outperforming a policy that also includes uncertain correct examples.
Predictive accuracy achieved with incremental active learning using both underconfident and overconfident examples
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Reconstructing Biological Pathways by Applying Selective Incremental Learning to (Very) Small Language Models
A small BERT model trained on only 117 of 517 curated regulatory relationships selected as confident errors reaches 93% balanced accuracy, outperforming a policy that also includes uncertain correct examples.