Discovering Data Structures: Nearest Neighbor Search and Beyond
📰 ArXiv cs.AI
Learn to implement end-to-end learning of data structures for nearest neighbor search and beyond, adapting to data distribution and controlling query and space complexity.
Action Steps
- Apply the proposed framework to learn data structures from scratch
- Use the framework to adapt to the underlying data distribution
- Implement fine-grained control over query and space complexity
- Evaluate the framework on nearest neighbor search problems
- Extend the framework to other data structure learning tasks
Who Needs to Know This
Data scientists and machine learning engineers can benefit from this framework to improve the efficiency of their algorithms and data structures. It can be applied to various settings, including nearest neighbor search.
Key Insight
💡 End-to-end learning of data structures can adapt to the underlying data distribution and provide fine-grained control over query and space complexity.
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🚀 Learn to implement end-to-end learning of data structures for nearest neighbor search and beyond! 🤖
Key Takeaways
Learn to implement end-to-end learning of data structures for nearest neighbor search and beyond, adapting to data distribution and controlling query and space complexity.
Full Article
Title: Discovering Data Structures: Nearest Neighbor Search and Beyond
Abstract:
arXiv:2411.03253v2 Announce Type: replace-cross Abstract: We propose a general framework for end-to-end learning of data structures. Our framework adapts to the underlying data distribution and provides fine-grained control over query and space complexity. Crucially, the data structure is learned from scratch, and does not require careful initialization or seeding with candidate data structures/algorithms. We first apply this framework to the problem of nearest neighbor search. In several settin
Abstract:
arXiv:2411.03253v2 Announce Type: replace-cross Abstract: We propose a general framework for end-to-end learning of data structures. Our framework adapts to the underlying data distribution and provides fine-grained control over query and space complexity. Crucially, the data structure is learned from scratch, and does not require careful initialization or seeding with candidate data structures/algorithms. We first apply this framework to the problem of nearest neighbor search. In several settin
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