APPENDIX: In this section, we provide the details of our implementation and proofs for reproducibility

's hidden state by h Then we need to calculate the second part of Eq. Using the Bayes' theorem, we have: p In Section 4.3, we devise a Sigmoid function to adapt the γ during the supernet training, which is defined as: γ (t) = 1 Sigmoidnull ( t total epochs 2 1) b null, (19) Section 3.2 theoretically demonstrates the benefit of the proposed architecture complementation loss function,