3 papers
cs.DS2026
Lower Bounds for the Algorithmic Complexity of Learned Indexes
Luis Alberto Croquevielle, Roman Sokolovskii, Thomas Heinis
Learned index structures aim to accelerate queries by training machine learning models to approximate the rank function associated with a database attribute. While effective in pra…
eess.IV2024
Single Exposure Quantitative Phase Imaging with a Conventional Microscope using Diffusion Models
Gabriel della Maggiora, Luis Alberto Croquevielle, Harry Horsley +2
Phase imaging is gaining importance due to its applications in fields like biomedical imaging and material characterization. In biomedical applications, it can provide quantitative…
cs.DB2024
Querying in Constant Expected Time with Learned Indexes
Luis Croquevielle, Guang Yang, Liang Liang +2
Learned indexes leverage machine learning models to accelerate query answering in databases, showing impressive practical performance. However, theoretical understanding of these m…