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

arXiv: https://arxiv.org/abs/2104.02234 · HTML: https://mlsanity.com/paper/2104.02234/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 64 references that are in the map.
- First posted on arXiv 6 Apr 2021.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Network Dissection: Quantifying Interpretability of Deep Visual Representations](https://mlsanity.com/paper/1704.05796/) (2017)
- [Understanding Neural Networks Through Deep Visualization](https://mlsanity.com/paper/1506.06579/) (2015)
- [Understanding the Role of Individual Units in a Deep Neural Network](https://mlsanity.com/paper/2009.05041/) (2020)
- [Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning](https://mlsanity.com/paper/1803.04765/) (2018)
- [Synthesizing the preferred inputs for neurons in neural networks via deep generator networks](https://mlsanity.com/paper/1605.09304/) (2016)
- [Object Detectors Emerge in Deep Scene CNNs](https://mlsanity.com/paper/1412.6856/) (2014)
- [Visualizing and Understanding Recurrent Networks](https://mlsanity.com/paper/1506.02078/) (2015)
- [Interpreting Deep Visual Representations via Network Dissection](https://mlsanity.com/paper/1711.05611/) (2017)
- [ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models](https://mlsanity.com/paper/1704.01942/) (2017)
- [Towards Better Analysis of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1604.07043/) (2016)
- [Going Deeper into First-Person Activity Recognition](https://mlsanity.com/paper/1605.03688/) (2016)
- [Revisiting the Importance of Individual Units in CNNs via Ablation](https://mlsanity.com/paper/1806.02891/) (2018)
- [Do semantic parts emerge in Convolutional Neural Networks?](https://mlsanity.com/paper/1607.03738/) (2016)
- [Interpreting Neural Networks With Nearest Neighbors](https://mlsanity.com/paper/1809.02847/) (2018)
- [KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics](https://mlsanity.com/paper/1610.09451/) (2016)
- [Helix: Holistic Optimization for Accelerating Iterative Machine Learning](https://mlsanity.com/paper/1812.05762/) (2018)
- [DeepBase: Deep Inspection of Neural Networks](https://mlsanity.com/paper/1808.04486/) (2018)

## Built on by (most cited first, 2 of 2)

- [MaskSearch: Querying Image Masks at Scale](https://mlsanity.com/paper/2305.02375/) (2023)
- [Demonstration of MaskSearch: Efficiently Querying Image Masks for Machine Learning Workflows](https://mlsanity.com/paper/2404.06563/) (2024)

## 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.