19 citations · 42 across the 10 of their papers we have counts for
11 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…
DiffML: End-to-end Differentiable ML Pipelines
Benjamin Hilprecht, Christian Hammacher, Eduardo Reis +2
In this paper, we present our vision of differentiable ML pipelines called DiffML to automate the construction of ML pipelines in an end-to-end fashion. The idea is that DiffML all…
Demonstrating CAT: Synthesizing Data-Aware Conversational Agents for Transactional Databases
Marius Gassen, Benjamin Hättasch, Benjamin Hilprecht +3
Databases for OLTP are often the backbone for applications such as hotel room or cinema ticket booking applications. However, developing a conversational agent (i.e., a chatbot-lik…
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…