4 papers
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…
Transferable Graph Metanetworks
Yuxin Ma, Adir Dayan, Yam Eitan +2
A weight space network (or metanetwork) takes the weights of another neural network as input and predicts properties of it. Most prior work trains such models on input networks of…
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…
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…