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Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning

Oldenburg, Valentijn et al.
arXiv (All) · Papers
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machine-learning
Finetuning methods for foundation models usually change weights, prompts, or hidden states, while leaving the residual topology fixed. We ask whether residual topology itself can become a finetuning object. To study this, we adapt manifold-constrained hyper-connections (mHC), recently introduced for
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