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University of Arizona
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SAMPLe significantly boosts the generalization of prompt learning in VLMs, outperforming traditional optimizers and mitigating overfitting issues.
Arbor's innovative approach to autonomous research enables a cumulative learning process that outperforms existing models by over 2.5 times in real-world tasks.
SOLAR shrinks PEFT adapter sizes without sacrificing performance by cleverly aligning adapter updates with the foundation model's intrinsic subspace.
