  This paper aims at one-shot learning of deep neural nets, where a highly
parallel setting is considered to address the algorithm calibration problem -
selecting the best neural architecture and learning hyper-parameter values
depending on the dataset at hand. The notoriously expensive calibration problem
is optimally reduced by detecting and early stopping non-optimal runs. The
theoretical contribution regards the optimality guarantees within the multiple
hypothesis testing framework. Experimentations on the Cifar10, PTB and Wiki
benchmarks demonstrate the relevance of the approach with a principled and
consistent improvement on the state of the art with no extra hyper-parameter.
