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Found 9 papers, 2 papers with code
SPARE: A Single-Pass Neural Model for Relational Databases
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.
Towards Foundation Models for Relational Databases [Vision Paper]
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.
DiffML: End-to-end Differentiable ML Pipelines
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.
Demonstrating CAT: Synthesizing Data-Aware Conversational Agents for Transactional Databases
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.
Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction
In this paper, we introduce zero-shot cost models which enable learned cost estimation that generalizes to unseen databases.
One Model to Rule them All: Towards Zero-Shot Learning for Databases
In this paper, we present our vision of so called zero-shot learning for databases which is a new learning approach for database components.
DeepDB: Learn from Data, not from Queries!
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
Reconstruction and Membership Inference Attacks against Generative Models
We present two information leakage attacks that outperform previous work on membership inference against generative models.
Model-based Approximate Query Processing
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.
