no code implementations • 20 Oct 2023 • 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.
no code implementations • 24 May 2023 • Liane Vogel, Benjamin Hilprecht, Carsten Binnig
However, existing approaches only learn a representation from a single table, and thus ignore the potential to learn from the full structure of relational databases, including neighboring tables that can contain important information for a contextualized representation.
no code implementations • 4 Jul 2022 • Benjamin Hilprecht, Christian Hammacher, Eduardo Reis, Mohamed Abdelaal, Carsten Binnig
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.
no code implementations • 26 Mar 2022 • Marius Gassen, Benjamin Hättasch, Benjamin Hilprecht, Nadja Geisler, Alexander Fraser, Carsten Binnig
However, developing a conversational agent (i. e., a chatbot-like interface) to allow end-users to interact with an application using natural language requires both immense amounts of training data and NLP expertise.
no code implementations • 3 Jan 2022 • Benjamin Hilprecht, Carsten Binnig
In this paper, we introduce zero-shot cost models which enable learned cost estimation that generalizes to unseen databases.
no code implementations • 3 May 2021 • 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.
1 code implementation • 2 Sep 2019 • Benjamin Hilprecht, Andreas Schmidt, Moritz Kulessa, Alejandro Molina, Kristian Kersting, Carsten Binnig
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.
Databases
1 code implementation • 7 Jun 2019 • Benjamin Hilprecht, Martin Härterich, Daniel Bernau
We present two information leakage attacks that outperform previous work on membership inference against generative models.
no code implementations • 15 Nov 2018 • Moritz Kulessa, Alejandro Molina, Carsten Binnig, Benjamin Hilprecht, Kristian Kersting
However, classical AQP approaches suffer from various problems that limit the applicability to support the ad-hoc exploration of a new data set: (1) Classical AQP approaches that perform online sampling can support ad-hoc exploration queries but yield low quality if executed over rare subpopulations.