DeepEverest: Accelerating Declarative Top-K Queries for Deep Neural Network Interpretation

A widely used interpretation by example We design, implement, and evaluate DeepEverest, a system for the query is, "find the top-inputs that produce the highest activation efficient execution of interpretation by example queries over the values for an individual neuron or a group of neurons" [12, 14, 21, activation values of a deep neural network. DeepEverest consists 33, 50, 57, 58, 61]. Another common query is, "for any input, find of an efficient indexing technique and a query execution algorithm the k-nearest neighbors in the dataset using the activation values of a with various optimizations. We prove that the proposed query group of neurons based on the proximity in the latent space defined execution algorithm is instance optimal.