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Found 2 papers, 1 papers with code
A-SFS: Semi-supervised Feature Selection based on Multi-task Self-supervision
Guided by the integrated information from the multi-self-supervised learning model, a batch-attention mechanism is designed to generate feature weights according to batch-based feature selection patterns to alleviate the impacts introduced by a handful of noisy data.
An Embedded Feature Selection Framework for Control
Reducing sensor requirements while keeping optimal control performance is crucial to many industrial control applications to achieve robust, low-cost, and computation-efficient controllers.
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