Deep generative models for structured outputs : from graphs and motion to language
Generative models learn the distribution of data and draw new samples from it. Deep generative models parameterize this distribution with neural networks, usually conditioned on an application-specific input. They are used in many domains, such as drug discovery, chat and coding assistants, robotics, and animation. Thi...