mlsanityData to 7 Oct 2026

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

arXiv 2104.02234 ↗First posted 6 Apr 2021cs.DB cs.LGcited by 2

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 27 of its 64 references that are in the map. First posted on arXiv 6 Apr 2021.

Builds on

27 of 64 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  4. 2013Intriguing properties of neural networkscited by 5,114
  5. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  6. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  7. 2017Billion-scale similarity search with GPUscited by 2,477
  8. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  9. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  10. 2016The Mythos of Model Interpretabilitycited by 716
  11. 2017Network Dissection: Quantifying Interpretability of Deep Visual Representationscited by 703
  12. 2015Understanding Neural Networks Through Deep Visualizationcited by 332
15 more
  1. 2020Understanding the Role of Individual Units in a Deep Neural Networkcited by 254
  2. 2018Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learningcited by 203
  3. 2016Synthesizing the preferred inputs for neurons in neural networks via deep generator networkscited by 181
  4. 2014Object Detectors Emerge in Deep Scene CNNscited by 178
  5. 2015Visualizing and Understanding Recurrent Networkscited by 158
  6. 2017Interpreting Deep Visual Representations via Network Dissectioncited by 103
  7. 2017ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Modelscited by 56
  8. 2016Towards Better Analysis of Deep Convolutional Neural Networkscited by 51
  9. 2016Going Deeper into First-Person Activity Recognitioncited by 48
  10. 2018Revisiting the Importance of Individual Units in CNNs via Ablationcited by 32
  11. 2016Do semantic parts emerge in Convolutional Neural Networks?cited by 23
  12. 2018Interpreting Neural Networks With Nearest Neighborscited by 18
  13. 2016KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analyticscited by 13
  14. 2018Helix: Holistic Optimization for Accelerating Iterative Machine Learningcited by 11
  15. 2018DeepBase: Deep Inspection of Neural Networkscited by 3

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2023MaskSearch: Querying Image Masks at Scalecited by 0
  2. 2024Demonstration of MaskSearch: Efficiently Querying Image Masks for Machine Learning Workflowscited by 0