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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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
DriftGuard introduces multi-monitor safety-aware drift detection paired with hard-mix selective adaptation, reporting toxic recall gains to 0.8777 on Civil Comments and 0.8523 on DynaHate under temporal and cross-dataset shifts.
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.
citing papers explorer
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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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DriftGuard: Safety-Aware Multi-Monitor Detection and Selective Adaptation for Evolving Toxicity Moderation
DriftGuard introduces multi-monitor safety-aware drift detection paired with hard-mix selective adaptation, reporting toxic recall gains to 0.8777 on Civil Comments and 0.8523 on DynaHate under temporal and cross-dataset shifts.
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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.