One of the principal goals of graph modeling is to capture the building blocks of network
data in order to study various physical and natural phenomena. Recent work at the …

We present a differentiable stack data structure that simultaneously and tractably encodes
an exponential number of stack configurations, based on Lang's algorithm for simulating …

An enormous amount of real-world data exists in the form of graphs. Oftentimes, interesting
patterns that describe the complex dynamics of these graphs are captured in the form of …

Traditional spatial enabled grammars lack flexibility in specifying the spatial semantics of
graphs. This paper describes a new graph grammar formalism called the multigranularity …

Graph models, like other machine learning models, have implicit and explicit biases built-in,
which often impact performance in nontrivial ways. The model's faithfulness is often …

Recent work at the intersection of formal language theory and graph theory has explored
graph grammars for graph modeling. However, existing models and formalisms can only …

Context-free graph grammars have shown a remarkable ability to model structures in real-
world relational data. However, graph grammars lack the ability to capture time-changing …

As robots find broader applications outside factory floors, they face an increasing number of
challenges. For example, they must accommodate rugged terrain, limited battery capacity …

We investigate the relational classification of biological cells in 2D microscopy images.
Rather than treating each cell image independently, we investigate whether and how the …

Discovering the underlying structures present in large real world graphs is a fundamental
scientific problem. Recent work at the intersection of formal language theory and graph …