Generative AI
We introduce a variational inference model called VIM, for Variational Independent Modules, for sequential data that learns and infers …
This paper explores the capabilities of current transformer-based language models for program evaluation of simple functional …
Large pre-trained language models for textual data have an unconstrained output space; at each decoding step, they can produce any of …
Explainability for machine learning models has gained considerable attention within the research community given the importance of …
Inferring objects and their relationships from an image in the form of a scene graph is useful in many applications at the intersection …
Recent work has made significant progress in learning object meshes with weak supervision. Soft Rasterization methods have achieved …
In a conversational question answering scenario, a questioner seeks to extract information about a topic through a series of …
Learning the causal structure that underlies data is a crucial step towards robust real-world decision making. The majority of existing …
Recent neural text-to-SQL models can effectively translate natural language questions to corresponding SQL queries on unseen databases. …
Negation is a core construction in natural language. Despite being very successful on many tasks, state-of-the-art pre-trained language …