Comments (2)
sarlinpe
commented on May 24, 2024
RoPE is indeed 1D. To adapt it to higher-dimensional data, some works like Lepard partition the feature space and treat each dimension individually, which is similar to a 1D sinusoidal encoding. As shown by Li et al, this introduces a bias along the basis axes. Using random learnable basis (Fourier Features) removes that bias and empirically learns a better data-dependent encoding.
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Master-cai
commented on May 24, 2024
Thank you for your patient! this confirms my thoughts.
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Related Issues (20)
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