Główne pojęcia
ニューラルネットワークを計算グラフとして表現し、パーミュテーション対称性を保持することで、異なるアーキテクチャに対応可能な効果的な手法を提案。
Statystyki
Neural networks that process the parameters of other neural networks find applications in domains as diverse as classifying implicit neural representations, generating neural network weights, and predicting generalization errors.
Neurons in a layer can be reordered while maintaining exactly the same function.
Graph neural networks and transformers naturally exhibit equivariance to the permutation symmetries of graphs.
Cytaty
"Neurons in a layer can be reordered while maintaining exactly the same function."
"Our approach enables a single model to learn from neural graphs with diverse architectures."