Zobrazeno 1 - 2
of 2
pro vyhledávání: '"Suleymanzade, Ayhan"'
We revisit a simple idea for machine learning on graphs, where a random walk on a graph produces a machine-readable record, and this record is processed by a deep neural network to directly make vertex-level or graph-level predictions. We refer to th
Externí odkaz:
http://arxiv.org/abs/2407.01214
We present a novel framework to overcome the limitations of equivariant architectures in learning functions with group symmetries. In contrary to equivariant architectures, we use an arbitrary base model such as an MLP or a transformer and symmetrize
Externí odkaz:
http://arxiv.org/abs/2306.02866