the Expressive Power of Permutation-Equivariant Weight-Space Networks
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On the Expressive Power of Permutation-Equivariant Weight-Space Networks
arXiv:2602.01083v2 Announce Type: replace Abstract: Weight-space learning studies neural architectures that operate directly on the parameters of other neural networks. Motivated by the growing availability of pretrained models, recent work has demonstrated the effectiveness of weight-space networks across a wide range of tasks. SOTA weight-space networks rely on permutation-equivariant designs to improve generalization.