Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
CrossGMN: Graph Metanetworks for Cross-Architecture Weight-Space Transformations
Adir Dayan, Yam Eitan, Haggai Maron
Weight-space networks operate directly on parameters of other neural networks, enabling tasks such as predicting model properties, editing trained models, and generating weights. W…
cs.LG2026
Finite Probes Suffice: Identifiability and Universality for Weight-Space Learning
Soutrik Sarangi, Yonatan Sverdlov, Adir Dayan +2
Learning properties of neural networks has recently attracted growing interest, with existing approaches operating either directly on network parameters or through probe-based repr…
cs.LG2026
On the Expressive Power of Permutation-Equivariant Weight-Space Networks
Adir Dayan, Yam Eitan, Haggai Maron
Weight-space learning studies neural architectures that operate directly on the parameters of other neural networks. Motivated by the growing availability of pretrained models, rec…