  This study aims at making the architecture search process more adaptive for
one-shot or online training. It is extended from the existing study on
differentiable neural architecture search, and we made the backbone
architecture transformable rather than fixed during the training process. As is
known, differentiable neural architecture search (DARTS) requires a pre-defined
over-parameterized backbone architecture, while its size is to be determined
manually. Also, in DARTS backbone, Hadamard product of two elements is not
introduced, which exists in both LSTM and GRU cells for recurrent nets. This
study introduces a growing mechanism for differentiable neural architecture
search based on network morphism. It enables growing of the cell structures
from small size towards large size ones with one-shot training. Two modes can
be applied in integrating the growing and original pruning process. We also
implement a recently proposed two-input backbone architecture for recurrent
neural networks. Initial experimental results indicate that our approach and
the two-input backbone structure can be quite effective compared with other
baseline architectures including LSTM, in a variety of learning tasks including
multi-variate time series forecasting and language modeling. On the other hand,
we find that dynamic network transformation is promising in improving the
efficiency of differentiable architecture search.
