  High sensitivity of neural architecture search (NAS) methods against their
input such as step-size (i.e., learning rate) and search space prevents
practitioners from applying them out-of-the-box to their own problems, albeit
its purpose is to automate a part of tuning process. Aiming at a fast, robust,
and widely-applicable NAS, we develop a generic optimization framework for NAS.
We turn a coupled optimization of connection weights and neural architecture
into a differentiable optimization by means of stochastic relaxation. It
accepts arbitrary search space (widely-applicable) and enables to employ a
gradient-based simultaneous optimization of weights and architecture (fast). We
propose a stochastic natural gradient method with an adaptive step-size
mechanism built upon our theoretical investigation (robust). Despite its
simplicity and no problem-dependent parameter tuning, our method exhibited near
state-of-the-art performances with low computational budgets both on image
classification and inpainting tasks.
