Joint Architecture And Hyper-Parameter Search For Machine Learning Models

摘要

The present disclosure provides a differentiable joint hyper-parameter and architecture search approach, with some implementations including the idea of discretizing the continuous space into a linear combination of multiple categorical basis. One example element of the proposed approach is the use of weight sharing across all architecture- and hyper-parameters which enables it to search efficiently over the large joint search space. Experimental results on MobileNet/ResNet/EfficientNet/BERT show that the proposed systems significantly improve the accuracy by up to 2% on ImageNet and the F1 by up to 0.4 on SQuAD, with search cost comparable to training a single model. Compared to other AutoML methods, such as random search or Bayesian method, the proposed techniques can achieve better accuracy with 10× less compute cost.