Standard few-shot learning methods employ geometric similarity metrics such as cosine similarity and negative Euclidean distance to gauge the semantic relatedness between two features.

Learning to detect and encode temporal regularities (TRs) in events is a prerequisite for human-like intelligence. These regularities should be formed from limited event samples and stored as easily retrievable representations.

Graph-structured data is ubiquitous in various disciplines [Gilmer et al., 2017, Zitnik et al., 2018, Sanchez-Gonzalez et al., 2020] and hence graph representation learning has the potential to provide

This mostly includes tasks with multifaceted objectives, such as dialogue response generation, or tasks with hard-to-define goals, such as enhancing program readability.

Such statements embed the notion of pairwise causality and can be better paraphrased as'occurrences of event type Z

ToolkenGPT offers the flexibility to plug in an arbitrary number of tools by expanding the set of toolkens on the fly. In addition, it improves tool use by allowing extensive demonstration data for learning the toolken embeddings.

Multi-view clustering (MVC) is a popular technique for improving clustering performance using various data sources.