Toaddress theabovechallenges, wepropose anovelhyperparameter mutation (HPM) scheduling algorithm in this study, which adopts a population based training framework to explicitly learn a trade-off (i.e., a mutation schedule) between using the hypergradient-guided local search and the mutation-driven global search.

A plant's phenome is defined by its physical and biochemical characteristics, and is the result of the interaction of its genome and its environment.

Because the deep learning models for the classification task always have a normalization operation (e.g., Softmax) to make the final unconstrained According to the definition of the Consistency of Representations Complexity Measure Eq.(??), itiseasy toseethatwehaveaninfinite number oflocal minima where themeasuresSi andMi,j for arbitrary classesiandj are finite positivenumbers. The python libraries we use to implement our experiments are PyTorch1.7.1andPyG1.6.3. C.2 DetailsofBaselineMethods In this subsection, we detailed the network architectures and the baseline methods used in our experiments. ImageNet is a benchmark dataset used for ResNet-50, which contains 14,197,122 annotated images with 1000 classes. For GNN, we selected four real-world graph datasets: PubMed [11] is a paper citation network where nodes represent documents and edgesrepresentcitationlinks.