Network embedding plays a significant role in a variety of applications. To capture the
topology of the network, most of the existing network embedding algorithms follow a …

Finding the proper depth $ d $ of a graph convolutional network (GCN) that provides strong
representation ability has drawn significant attention, yet nonetheless largely remains an …

Self-supervised learning (SSL) for graphs is an essential problem since graph data are
ubiquitous and labeling can be costly. We argue that existing SSL approaches for graphs …

Although database systems perform well in data access and manipulation, their relational
model hinders data scientists from formulating machine learning algorithms in SQL …

We consider the problem of automatically decomposing operations over tensors or arrays so
that they can be executed in parallel on multiple devices. We address two, closely-linked …

Although database systems perform well in data access and manipulation, their relational
model hinders data scientists from formulating machine learning algorithms in SQL …

Serving deep learning (DL) models on relational data has become a critical requirement
across diverse commercial and scientific domains, sparking growing interest recently. In this …

We introduce StarfishDB, a query execution engine optimized for relational probabilistic
programming. Our engine adopts the model of Gamma Probabilistic Databases …

Model databases track the accuracy of models on pre-trained weights. The models are
stored as executable code and extracted on deployment. Instead of extracting runnable …