(For any questions please email deepgenstruct2019@gmail.com)

Deep generative models are at the core of research in artificial intelligence. They have achieved remarkable performance in many domains including computer vision, speech recognition, audio synthesis, and natural language processing. In recent years, they have also infiltrated other fields of science including the natural sciences, physics, chemistry, molecular biology, and medicine. Despite these successes, deep generative models still face many challenges when they are used to model highly structured data such as natural language, video, and generic graph-structured data such as molecules. These challenges include tractable algorithms for learning and inference, domain-specific parameterizations of generative models, rigorous evaluation of generative models, and more. This first workshop on Deep Generative Models for Highly Structured Data aims to bring experts from different backgrounds and perspectives to discuss the applications of deep generative models to these data modalities.

Relevant topics to this workshop include but are not limited to: