19 citations · 39 across the 7 of their papers we have counts for
8 papers
SPARE: A Single-Pass Neural Model for Relational Databases
Benjamin Hilprecht, Kristian Kersting, Carsten Binnig
While there has been extensive work on deep neural networks for images and text, deep learning for relational databases (RDBs) is still a rather unexplored field. One direction tha…
Towards Foundation Models for Relational Databases [Vision Paper]
Liane Vogel, Benjamin Hilprecht, Carsten Binnig
Tabular representation learning has recently gained a lot of attention. However, existing approaches only learn a representation from a single table, and thus ignore the potential…
Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction
Benjamin Hilprecht, Carsten Binnig
In this paper, we introduce zero-shot cost models which enable learned cost estimation that generalizes to unseen databases. In contrast to state-of-the-art workload-driven approac…
ReStore -- Neural Data Completion for Relational Databases
Benjamin Hilprecht, Carsten Binnig
Classical approaches for OLAP assume that the data of all tables is complete. However, in case of incomplete tables with missing tuples, classical approaches fail since the result…
One Model to Rule them All: Towards Zero-Shot Learning for Databases
Benjamin Hilprecht, Carsten Binnig
In this paper, we present our vision of so called zero-shot learning for databases which is a new learning approach for database components. Zero-shot learning for databases is ins…
DeepDB: Learn from Data, not from Queries!
Benjamin Hilprecht, Andreas Schmidt, Moritz Kulessa +3
The typical approach for learned DBMS components is to capture the behavior by running a representative set of queries and use the observations to train a machine learning model. T…