1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 1 cited
Graph Metanetworks for Processing Diverse Neural Architectures
Derek Lim, Haggai Maron, Marc T. Law +2
Neural networks efficiently encode learned information within their parameters. Consequently, many tasks can be unified by treating neural networks themselves as input data. When d…
cs.LG2023
Data Augmentations in Deep Weight Spaces
Aviv Shamsian, David W. Zhang, Aviv Navon +10
Learning in weight spaces, where neural networks process the weights of other deep neural networks, has emerged as a promising research direction with applications in various field…
cs.LG2023★ 1 cited
Equivariant Polynomials for Graph Neural Networks
Omri Puny, Derek Lim, Bobak T. Kiani +2
Graph Neural Networks (GNN) are inherently limited in their expressive power. Recent seminal works (Xu et al., 2019; Morris et al., 2019b) introduced the Weisfeiler-Lehman (WL) hie…