  We develop, implement, and evaluate DeepEverest, a system for the efficient
execution of interpretation by example queries over the activation values of a
deep neural network. DeepEverest consists of an efficient indexing technique
and a query execution algorithm with various optimizations. Experiments with
our prototype implementation show that DeepEverest, using less than 20% of the
storage of full materialization, significantly accelerates individual queries
by up to 62x and consistently outperforms other methods on multi-query
workloads that simulate DNN interpretation processes.
