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University of Illinois Urbana-Champaign
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ApproxHDC reveals that automated approximation tuning in Hyperdimensional Computing can yield significant performance boosts without sacrificing accuracy, challenging conventional deep learning paradigms.
Retrieving the right prompts can boost LMM performance by up to 30%, challenging the assumption that similarity guarantees effectiveness in in-context learning.
