High-Value Training Data from Noisy, Diverse Sources
Training state-of-the-art models increasingly requires learning from large, heterogeneous datasets whose examples vary in quality, domain relevance, and source of supervision. Dataweighting---assigning different weights to training examples---offers a general way to control how such data influences training, but learni...
Hedi Driss, Evan Frick, Anastasios Nikolas Angelopoulos et al.
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