Graphs
We propose an interpretable local surrogate (ILS) method for understanding the predictions of black-box graph models. Explainability …
We propose a continuous optimization framework for discovering a latent directed acyclic graph (DAG) from observational data. Our …
Embedding-based methods for reasoning in knowledge hypergraphs learn a representation for each entity and relation. Current methods do …
We explore the downstream task performances for graph neural network (GNN) self-supervised learning (SSL) methods trained on subgraphs …
We extend score-based generative diffusion processes (GDPs) to sparse graphs and other inherently discrete data, with a focus on …
Recently continuous relaxations have been proposed in order to learn directed acyclic graphs (DAGs) by backpropagation, instead of …
We explore the downstream task performances for graph neural network (GNN) self-supervised learning (SSL) methods trained on subgraphs …
Semantic parsing is the task of producing a structured meaning representation for natural language utterances or questions. Recent …
Recent research suggests that systematic generalization in natural language understanding remains a challenge for state-of-the-art …
Inferring objects and their relationships from an image in the form of a scene graph is useful in many applications at the intersection …