DARE-EEG is a self-supervised EEG foundation model that enforces mask-invariance via contrastive mask alignment and momentum anchor alignment, plus conv-linear-probing for heterogeneous setups, achieving SOTA accuracy and cross-dataset portability.
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects,
2 Pith papers cite this work. Polarity classification is still indexing.
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An algorithm selects traffic counter locations to increase observed traffic-pattern diversity; real-world installation of the chosen counters improved volume estimation accuracy.
citing papers explorer
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DARE-EEG: A Foundation Model for Mining Dual-Aligned Representation of EEG
DARE-EEG is a self-supervised EEG foundation model that enforces mask-invariance via contrastive mask alignment and momentum anchor alignment, plus conv-linear-probing for heterogeneous setups, achieving SOTA accuracy and cross-dataset portability.
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Selecting New Measurement Locations to Diversify Traffic-Pattern Coverage: A Real-World Evaluation for Total Traffic Volume Estimation
An algorithm selects traffic counter locations to increase observed traffic-pattern diversity; real-world installation of the chosen counters improved volume estimation accuracy.