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W^{*}=(Y-Y_{Z})X_{Z}^{T}(X_{Z}X_{Z}^{T}+\lambda I)^{-1}\,,italic_W start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT = ( italic_Y - italic_Y start_POSTSUBSCRIPT italic_Z end_POSTSUBSCRIPT ) italic_X start_POSTSUBSCRIPT italic_Z end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_T end_POSTSUPERSCRIPT ( italic_X start_POSTSUBSCRIPT italic_Z end_POSTSUBSCRIPT italic_X start_POSTSUBSCRIPT italic_Z end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_T end_POSTSUPERSCRIPT + italic_λ italic_I ) start_POSTSUPERSCRIPT - 1 end_POSTSUPERSCRIPT , (3) which includes a regularization term λI𝜆𝐼\lambda Iitalic_λ italic_I for stability. In our W, School of Artificial Intelligence, Nanjing University, China
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Quantizing Vision Transformers to 4-bit precision no longer requires a painful trade-off between accuracy, speed, and memory, thanks to a new activation-first training method that's 100x faster.
