ETD

• ann
• dense vector
• embedding
• information retrieval

This work investigates dimensionality reduction techniques for dense embedding vectors as a training free alternative to learned approaches. The goal is to improve neural information retrieval by increasing retrieval speed and quality on large corpora, while enabling approximate nearest neighbor search on resource constrained edge devices without retraining embedding