  Deep learning has been widely used in various applications from different
fields such as computer vision, natural language processing, etc. However, the
training models are often manually developed via many costly experiments. This
manual work usually requires substantial computing resources, time, and
experience. To simplify the use of deep learning and alleviate human effort,
automated deep learning has emerged as a potential tool that releases the
burden for both users and researchers. Generally, an automatic approach should
support the diversity of model selection and the evaluation should allow users
to decide upon their demands. To that end, we propose a multi-objective
end-to-end self-optimized approach for constructing deep learning models
automatically. Experimental results on well-known datasets such as MNIST,
Fashion, and Cifar10 show that our algorithm can discover various competitive
models compared with the state-of-the-art approach. In addition, our approach
also introduces multi-objective trade-off solutions for both accuracy and
uncertainty metrics for users to make better decisions.
