  Networks found with Neural Architecture Search (NAS) achieve state-of-the-art
performance in a variety of tasks, out-performing human-designed networks.
However, most NAS methods heavily rely on human-defined assumptions that
constrain the search: architecture's outer-skeletons, number of layers,
parameter heuristics and search spaces. Additionally, common search spaces
consist of repeatable modules (cells) instead of fully exploring the
architecture's search space by designing entire architectures (macro-search).
Imposing such constraints requires deep human expertise and restricts the
search to pre-defined settings. In this paper, we propose LCMNAS, a method that
pushes NAS to less constrained search spaces by performing macro-search without
relying on pre-defined heuristics or bounded search spaces. LCMNAS introduces
three components for the NAS pipeline: i) a method that leverages information
about well-known architectures to autonomously generate complex search spaces
based on Weighted Directed Graphs with hidden properties, ii) an evolutionary
search strategy that generates complete architectures from scratch, and iii) a
mixed-performance estimation approach that combines information about
architectures at initialization stage and lower fidelity estimates to infer
their trainability and capacity to model complex functions. We present
experiments in 13 different data sets showing that LCMNAS is capable of
generating both cell and macro-based architectures with minimal GPU computation
and state-of-the-art results. More, we conduct extensive studies on the
importance of different NAS components in both cell and macro-based settings.
Code for reproducibility is public at https://github.com/VascoLopes/LCMNAS.
