#label-efficiency News & Analysis

3 articles tagged with #label-efficiency. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AIBullisharXiv – CS AI · Jun 27/10

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EvoPool: Evolutionary Programmatic Annotation for Label-Efficient Specialized Supervision

EvoPool is an evolutionary multi-agent framework that generates specialized annotation code to label training data more efficiently than LLMs for domain-specific tasks. The system operates 4,500-31,000x faster than LLM annotation while achieving superior performance across biomedical, legal, and reasoning tasks, with improvements up to +0.301 macro-F1 on specialized benchmarks.

AINeutralarXiv – CS AI · Jun 236/10

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SLeDGe: Semi-Supervised Learning on Data Streams with Graph Structure Learning

Researchers introduce SLeDGe, a semi-supervised learning method designed for streaming data that dynamically learns graph structures to capture evolving relationships between samples. The approach achieves significant accuracy improvements (31.7% relative gain with 0.1% labels) by balancing memory constraints with adaptive graph learning, addressing a key limitation in existing SSL methods that rely on static similarity measures.

AINeutralarXiv – CS AI · Jun 196/10

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SL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models

Researchers introduce SL-S4Wave, a self-supervised learning framework combining contrastive learning with structured state space models to analyze physiological waveforms like ECGs and EEGs. The approach outperforms existing methods in detecting arrhythmias, requires fewer labeled examples, and generalizes effectively across different cardiac conditions and brain signals.