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ALPHABET achieves Bayes oracle performance with a mere 6,437 parameters, revolutionizing efficiency in sequence modeling.
TESLA not only solves the parity problem with minimal data but also excels in robustness, outperforming traditional methods under label noise.
Shifting from pixel-centric to class-centric exploration, SegPAR achieves unprecedented efficiency in sparse attacks for semantic segmentation, outperforming existing methods.
