AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural Networks

1 code implementation • 14 Mar 2023 • Kaidi Cao, Jiaxuan You, Jiaju Liu, Jure Leskovec

Experiments demonstrate that (i) our proposed task embedding can be computed efficiently, and that tasks with similar embeddings have similar best-performing architectures; (ii) AutoTransfer significantly improves search efficiency with the transferred design priors, reducing the number of explored architectures by an order of magnitude.

AutoML Transfer Learning