The field of quantization and estimation is moving towards developing innovative techniques to improve the accuracy and efficiency of various applications. Researchers are exploring new methods to optimize vector quantization, such as TurboQuant, which achieves near-optimal distortion rates. Additionally, there is a growing interest in applying machine learning techniques, like focal loss, to lossy source coding. The use of James Stein shrinkage is also being investigated for optimal vector compressed sensing. Furthermore, affine matrix scrambling is being studied for its ability to achieve smoothness-dependent convergence rates. Notable papers in this area include TurboQuant, which overcomes limitations of existing vector quantization methods, and Optimal Vector Compressed Sensing Using James Stein Shrinkage, which proposes a lightweight iterative algorithm for optimal vector recovery.
Advances in Quantization and Estimation Techniques
Sources
Empirical Bernstein and betting confidence intervals for randomized quasi-Monte Carlo
Learning High-dimensional Gaussians from Censored Data
TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate
Lossy Source Coding with Focal Loss
MINT: Multi-Vector Search Index Tuning
Handling Large-Scale Network Flow Records: A Comparative Study on Lossy Compression
Optimization of embeddings storage for RAG systems using quantization and dimensionality reduction techniques
Optimal Vector Compressed Sensing Using James Stein Shrinkage
Affine matrix scrambling achieves smoothness-dependent convergence rates