MOD

While a lot of work has focused on improving the efficiency, scalability, and usability of machine learning (ML), little work has studied the cost of data acquisition for ML-based analytics. Datasets are already being bought and sold in marketplaces for ...

End-to-end machine learning pipelines that run in shared environments are challenging to implement. Production pipelines typically consist of multiple interdependent processing stages. Between stages, the intermediate results are persisted to reduce ...

Relational databases are valuable resources for learning novel and interesting relations and concepts. Relational learning algorithms learn the definition of new relations in terms of the existing relations in the database. In order to constraint the ...

Machine translation, at its core, is a Machine Learning (ML) problem that involves learning language translation by looking at large amounts of parallel data i.e. translations of the same dataset in two or more languages. If we have parallel data ...

We investigate opportunities for exploiting Artificial Intelligence (AI) techniques for enhancing capabilities of relational databases. In particular, we explore applications of Natural Language Processing (NLP) techniques to endow relational databases ...

Alongside developing systems for scalable machine learning and collaborative data science activities, there is an increasing trend toward publicly shared data science projects, hosted in general or dedicated hosting services, such as GitHub and DataHub. ...