MOD

Recent advances in machine learning (ML) systems have made it incredibly easier to train ML models given a training set. However, our understanding of the behavior of the model training process has not been improving at the same pace. Consequently, a ...

We present sql4ml, a framework for expressing machine learning (ML) algorithms in a relational database management system (RDBMS). The user writes the objective function of an ML model as a SQL query, then sql4ml translates the query into an equivalent ...

As neural networks are increasingly employed in machine learning practice, how to efficiently share limited training resources among a diverse set of model training tasks becomes a crucial issue. To achieve better utilization of the shared resources, we ...

In this work, we use semantic knowledge sources, such as cross-domain knowledge graphs (KGs) and domain-specific ontologies, to enrich structured data for various AI applications. By enriching our understanding of the underlying data with semantics ...

The potential harms and drawbacks of automated decision making has become a challenge as data science blends into our lives. In particular, fairness issues with deployed machine learning models have drawn significant attention from the research ...

In our institute we capture a variety of medical image data - in particular, microscopy data for the use case of bronchoconstriction. In order to alleviate the manual intervention for the image analysis, we developed a comprehensive data analysis ...