DeepEverest: Accelerating Declarative Top-K Queries for Deep Neural Network Interpretation
Summary: Targets declarative top-K “interpretation-by-example” queries over DNN activations via a compact indexing scheme and optimized execution. Instance-optimal algorithm + <20% materialization cost yields up to 63x single-query speedups and consistent multi-query dominance. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Dong He
- 2. Maureen Daum
- 3. Walter Cai
- 4. Magdalena Balazinska
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,383 | EQUI-VOCAL: Synthesizing Queries for Compositional Video Events from Limited User Interactions | 2023 | VLDB | 4.5307128e-05 |
| 10,503 | Self-Enhancing Video Data Management System for Compositional Events with Large Language Models | 2025 | SIGMOD | 4.1945683e-05 |
| 11,008 | MetaStore: Analyzing Deep Learning Meta-Data at Scale | 2024 | VLDB | 4.1945683e-05 |
| 11,099 | Demonstration of MaskSearch: Efficiently Querying Image Masks for Machine Learning Workflows | 2024 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2 | R-Trees: A Dynamic Index Structure For Spatial Searching | 1984 | SIGMOD | 0.0032169493 |
| 7 | Optimal Aggregation Algorithms for Middleware [Extended Abstract] | 2001 | PODS | 0.0015496097 |
| 1,413 | VisTrails: Visualization meets Data Management | 2006 | SIGMOD | 0.00012121257 |
| 1,666 | HELIX: Holistic Optimization for Accelerating Iterative Machine Learning | 2019 | VLDB | 0.0001096361 |
| 1,808 | Top-k Query Evaluation with Probabilistic Guarantees | 2004 | VLDB | 0.00010486213 |
| 2,009 | IO-Top-k: Index-access Optimized Top-k Query Processing | 2006 | VLDB | 9.7977564e-05 |
| 2,152 | MISTIQUE: A System to Store and Query Model Intermediates for Model Diagnosis | 2018 | SIGMOD | 9.4239787e-05 |
| 2,883 | Joining Ranked Inputs in Practice | 2002 | VLDB | 7.9656673e-05 |
| 4,186 | Best Position Algorithms for Top-k Queries | 2007 | VLDB | 6.3764858e-05 |
| 6,373 | DeepBase: Deep Inspection of Neural Networks | 2019 | SIGMOD | 5.0929326e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,350 | DeepO: A Learned Query Optimizer | 2022 | SIGMOD | 4.1945683e-05 |
| 7,061 | Serving Deep Learning Models with Deduplication from Relational Databases | 2022 | VLDB | 4.8463881e-05 |
| 5,473 | Facilitating SQL Query Composition and Analysis | 2020 | SIGMOD | 5.4885366e-05 |
| 6,230 | Learned Approximate Query Processing: Make it Light, Accurate and Fast | 2021 | CIDR | 5.145989e-05 |
| 3,658 | Towards a Hands-Free Query Optimizer through Deep Learning | 2019 | CIDR | 6.8704209e-05 |
| 13,229 | Using Deep Learning Models to Replace Large Materialized Views in Relational Database | 2021 | CIDR | - |
| 884 | Plan-Structured Deep Neural Network Models for Query Performance Prediction | 2019 | VLDB | 0.00015654004 |
| 6,182 | Top-K Deep Video Analytics: A Probabilistic Approach | 2021 | SIGMOD | 5.1682689e-05 |
| 9,120 | Deep Query Optimization | 2019 | SIGMOD | 4.392741e-05 |
| 9,770 | Everest: A Top-K Deep Video Analytics System | 2022 | SIGMOD | 4.2856106e-05 |