Google llc (20240119052). Tuning Approximate Nearest Neighbor Search Engines for Speed-Recall Tradeoffs Via Lagrange Multiplier Methods simplified abstract
Contents
- 1 Tuning Approximate Nearest Neighbor Search Engines for Speed-Recall Tradeoffs Via Lagrange Multiplier Methods
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Tuning Approximate Nearest Neighbor Search Engines for Speed-Recall Tradeoffs Via Lagrange Multiplier Methods - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
Tuning Approximate Nearest Neighbor Search Engines for Speed-Recall Tradeoffs Via Lagrange Multiplier Methods
Organization Name
Inventor(s)
Philip Wenjie Sun of New York NY (US)
Sanjiv Kumar of Jericho NY (US)
Tuning Approximate Nearest Neighbor Search Engines for Speed-Recall Tradeoffs Via Lagrange Multiplier Methods - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240119052 titled 'Tuning Approximate Nearest Neighbor Search Engines for Speed-Recall Tradeoffs Via Lagrange Multiplier Methods
Simplified Explanation
The disclosure focuses on automatically tuning quantization-based approximate nearest neighbors (ANN) search methods and systems to perform at the speed-recall pareto frontier. By employing Lagrangian-based methods for constrained optimization on search cost and recall models, the embodiments achieve excellent performance on standard benchmarks with minimal tuning complexity.
- Efficient quantization-based ANN implementation
- Lagrangian-based methods for constrained optimization
- Theoretically-grounded search cost and recall models
- Excellent performance on standard benchmarks
- Minimal tuning or configuration complexity
Potential Applications
This technology can be applied in various fields such as image and video retrieval, recommendation systems, data mining, and machine learning.
Problems Solved
1. Optimizing search methods for speed-recall trade-offs 2. Minimizing tuning complexity in ANN search systems
Benefits
1. Improved performance on standard benchmarks 2. Efficient search cost and recall optimization 3. Minimal tuning or configuration requirements
Potential Commercial Applications
Optimizing search engines, enhancing recommendation systems, improving data mining algorithms, and advancing machine learning models.
Possible Prior Art
Prior art may include research on optimization techniques for ANN search methods, quantization-based algorithms for nearest neighbor search, and theoretical models for search cost and recall optimization.
Unanswered Questions
How does this technology compare to other state-of-the-art ANN search methods in terms of performance and efficiency?
Further comparative studies and benchmarking are needed to evaluate the effectiveness of this technology against existing approaches.
What are the potential limitations or constraints of implementing this technology in real-world applications?
Exploring the scalability, adaptability, and computational requirements of this technology in large-scale systems and diverse use cases is essential for practical deployment.
Original Abstract Submitted
the disclosure is directed towards automatically tuning quantization-based approximate nearest neighbors (ann) search methods and systems (e.g., search engines) to perform at the speed-recall pareto frontier. with a desired search cost or recall as input, the embodiments employ lagrangian-based methods to perform constrained optimization on theoretically-grounded search cost and recall models. the resulting tunings, when paired with the efficient quantization-based ann implementation of the embodiments, exhibit excellent performance on standard benchmarks while requiring minimal tuning or configuration complexity.
