An active learning method based on E-SINDy identifies governing ODEs and PDEs accurately with significantly fewer data samples than random sampling across tested systems.
Learning from noisy examples
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Proposes the EL-MIATTs framework for ML predictive modeling by assuming the true target does not exist and defining democratic supervision via multiple inaccurate true targets.
citing papers explorer
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How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit
An active learning method based on E-SINDy identifies governing ODEs and PDEs accurately with significantly fewer data samples than random sampling across tested systems.
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Negative Ontology of True Target for Machine Learning: Towards Evaluation and Learning under Democratic Supervision
Proposes the EL-MIATTs framework for ML predictive modeling by assuming the true target does not exist and defining democratic supervision via multiple inaccurate true targets.