Some examples of vector databases that have mitigated the curse of dimensionality include:
- Google's BERT: BERT uses a multi-layer bidirectional transformer encoder to reduce the dimensionality of the input vectors and improve performance.
- Facebook's AI: Facebook's AI uses a combination of dimensionality reduction and indexing techniques to improve the efficiency of its vector database.
- Amazon's SageMaker: SageMaker uses a combination of caching, data compression, and approximation methods to improve the performance of its vector databases.