  Deep learning (DL) models have achieved paradigm-changing performance in many
fields with high dimensional data, such as images, audio, and text. However,
the black-box nature of deep neural networks is a barrier not just to adoption
in applications such as medical diagnosis, where interpretability is essential,
but also impedes diagnosis of under performing models. The task of diagnosing
or explaining DL models requires the computation of additional artifacts, such
as activation values and gradients. These artifacts are large in volume, and
their computation, storage, and querying raise significant data management
challenges.
  In this paper, we articulate DL diagnosis as a data management problem, and
we propose a general, yet representative, set of queries to evaluate systems
that strive to support this new workload. We further develop a novel data
sampling technique that produce approximate but accurate results for these
model debugging queries. Our sampling technique utilizes the lower dimension
representation learned by the DL model and focuses on model decision boundaries
for the data in this lower dimensional space. We evaluate our techniques on one
standard computer vision and one scientific data set and demonstrate that our
sampling technique outperforms a variety of state-of-the-art alternatives in
terms of query accuracy.
