Paper: Width-Independent Compressibility of Deep Neural Networks
burny_tech · x · 2026-09-02
Liu Ziyin et al. published a paper proving that deep MLPs can be compressed to a size dependent only on input dimension, independent of model width. The study introduces a novel derivative-matching technique and layer-wise reweighting, demonstrating uniform compressibility for deep networks with analytic activations.
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