Term
Sparse Attention
Definition
Sparse Attention is a technique in artificial intelligence where the system focuses on a specific, smaller part of the input data instead of considering everything at once. This helps the AI process information faster and more efficiently.
Where you’ll find it
Sparse Attention is typically embedded within AI models, particularly in settings where processing large datasets efficiently is crucial. It is often adjustable in training parameters or found in the optimization features of AI platforms.
Common use cases
- In natural language processing models to improve performance without losing accuracy.
- For image recognition tasks to quickly identify relevant features without analyzing the entire image.
- In real-time AI applications where speed is essential for performance.
Things to watch out for
- Choosing the wrong subset of data for focus can lead to poor model performance.
- It may not be supported by all AI frameworks; availability can vary.
- Proper configuration is critical; if it is too sparse, you might miss important data.
Related terms
- Attention Mechanism
- Model Optimization
- Neural Networks
- Data Efficiency
- AI Acceleration