  The success of neural architecture search (NAS) has historically been limited
by excessive compute requirements. While modern weight-sharing NAS methods such
as DARTS are able to finish the search in single-digit GPU days, extracting the
final best architecture from the shared weights is notoriously unreliable.
Training-Speed-Estimate (TSE), a recently developed generalization estimator
with a Bayesian marginal likelihood interpretation, has previously been used in
place of the validation loss for gradient-based optimization in DARTS. This
prevents the DARTS skip connection collapse, which significantly improves
performance on NASBench-201 and the original DARTS search space. We extend
those results by applying various DARTS diagnostics and show several unusual
behaviors arising from not using a validation set. Furthermore, our experiments
yield concrete examples of the depth gap and topology selection in DARTS having
a strongly negative impact on the search performance despite generally
receiving limited attention in the literature compared to the operations
selection.
