Summary of Discovering Data Structures: Nearest Neighbor Search and Beyond, by Omar Salemohamed et al.
Discovering Data Structures: Nearest Neighbor Search and Beyond
by Omar Salemohamed, Laurent Charlin, Shivam Garg, Vatsal Sharan, Gregory Valiant
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Data Structures and Algorithms (cs.DS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed general framework enables end-to-end learning of data structures, adapting to underlying data distributions while providing fine-grained control over query and space complexity. This framework learns data structures from scratch, without requiring careful initialization or seeding with candidate data structures/algorithms. The authors apply this framework to nearest neighbor search, successfully reverse-engineering learned data structures and query algorithms in various settings. In 1D searches, the model discovers optimal distribution-independent algorithms such as binary search and variants of interpolation search. In higher dimensions, the model learns solutions resembling k-d trees or locality-sensitive hashing. Additionally, the framework can learn useful representations of high-dimensional data, exploiting them to design effective data structures. The authors also adapt their framework to estimating frequencies over a data stream, demonstrating its potential as a powerful discovery tool for new problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a system that can automatically learn how to organize and search through large amounts of data. This is important because it allows the system to find the best way to solve a problem without needing human input. The system was tested on two different tasks: finding the closest match in a database, and estimating how often certain types of data appear in a stream of information. In both cases, the system was able to learn the most effective way to complete the task. |
Keywords
» Artificial intelligence » Nearest neighbor
