Presents a diversity-aware batch-mode query-by-committee active learning method using cosine similarity to select non-redundant queries for efficient stress-space sampling in data-driven constitutive modeling.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Active learning for chemical reaction extraction frequently produces non-monotonic learning curves and fails to deliver stable gains over random sampling because of strong pretraining, structured CRF decoding, and label sparsity.
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Diversity-Aware Batch-Mode Active Learning for Efficient Sampling in Data-Driven Constitutive Modeling
Presents a diversity-aware batch-mode query-by-committee active learning method using cosine similarity to select non-redundant queries for efficient stress-space sampling in data-driven constitutive modeling.
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When Active Learning Falls Short: An Empirical Study on Chemical Reaction Extraction
Active learning for chemical reaction extraction frequently produces non-monotonic learning curves and fails to deliver stable gains over random sampling because of strong pretraining, structured CRF decoding, and label sparsity.