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

Autor:	Dong He, Maureen Daum, Walter Cai, Magdalena Balazinska
Rok vydání:	2021
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning Computer Science - Databases General Engineering Databases (cs.DB) Machine Learning (cs.LG)
DOI:	10.48550/arxiv.2104.02234
Popis:	We design, 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. We prove that the proposed query execution algorithm is instance optimal. Experiments with our prototype show that DeepEverest, using less than 20% of the storage of full materialization, significantly accelerates individual queries by up to 63x and consistently outperforms other methods on multi-query workloads that simulate DNN interpretation processes. Comment: This is an extended technical report for the following paper: "DeepEverest: Accelerating Declarative Top-K Queries for Deep Neural Network Interpretation. PVLDB, 15(1): 98 - 111, 2021. doi:10.14778/3485450.3485460"
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5837cbb3c3c576bb8db897a43ba36d9c Zobrazit plný text záznamu